Projekt z Zaawansowanej Eksploracji Danych
Rafał Morawski
28.11.2020
Wynik
Rozwiazanie
Wykorzystane biblioteki
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(tidyr)
library(knitr)
library(DescTools)
library(readxl)
library(caret)## Loading required package: lattice## Loading required package: ggplot2##
## Attaching package: 'caret'## The following objects are masked from 'package:DescTools':
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## MAE, RMSElibrary(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
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## group_rowsWczytywanie dannych, zmianna nazw kolumn
Z pakietu ‘readxl’ wykorzystano funkcję ‘read_excel’. Jak się okazuje, wiersze dla kolumny ‘PATIENT_ID’ są złączone ze sobą w excelu, natomiast po wczytaniu tylko jeden wiersz dla danego id nie otrzymuje wartość NaN.
orig_data_csv <- read_excel("wuhan_blood_sample_data_Jan_Feb_2020.xlsx")
kable(head(orig_data_csv, 5)) %>%
kable_styling(bootstrap_options = "basic",
full_width = F) %>%
scroll_box(width = "100%", height = "100%")## Warning in do.call(data.frame, c(x, alis)): unable to translate 'Tumor necrosis
## factor<U+03B1>' to native encoding## Warning in do.call(data.frame, c(x, alis)): unable to translate '<U+03B3>-
## glutamyl transpeptidase' to native encoding| PATIENT_ID | RE_DATE | age | gender | Admission time | Discharge time | outcome | Hypersensitive cardiac troponinI | hemoglobin | Serum chloride | Prothrombin time | procalcitonin | eosinophils(%) | Interleukin 2 receptor | Alkaline phosphatase | albumin | basophil(%) | Interleukin 10 | Total bilirubin | Platelet count | monocytes(%) | antithrombin | Interleukin 8 | indirect bilirubin | Red blood cell distribution width | neutrophils(%) | total protein | Quantification of Treponema pallidum antibodies | Prothrombin activity | HBsAg | mean corpuscular volume | hematocrit | White blood cell count | Tumor necrosis factorα | mean corpuscular hemoglobin concentration | fibrinogen | Interleukin 1β | Urea | lymphocyte count | PH value | Red blood cell count | Eosinophil count | Corrected calcium | Serum potassium | glucose | neutrophils count | Direct bilirubin | Mean platelet volume | ferritin | RBC distribution width SD | Thrombin time | (%)lymphocyte | HCV antibody quantification | D-D dimer | Total cholesterol | aspartate aminotransferase | Uric acid | HCO3- | calcium | Amino-terminal brain natriuretic peptide precursor(NT-proBNP) | Lactate dehydrogenase | platelet large cell ratio | Interleukin 6 | Fibrin degradation products | monocytes count | PLT distribution width | globulin | γ-glutamyl transpeptidase | International standard ratio | basophil count(#) | 2019-nCoV nucleic acid detection | mean corpuscular hemoglobin | Activation of partial thromboplastin time | High sensitivity C-reactive protein | HIV antibody quantification | serum sodium | thrombocytocrit | ESR | glutamic-pyruvic transaminase | eGFR | creatinine |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2020-01-31 01:09:00 | 73 | 1 | 2020-01-30 22:12:47 | 2020-02-17 12:40:09 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 7.415 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| NA | 2020-01-31 01:25:00 | 73 | 1 | 2020-01-30 22:12:47 | 2020-02-17 12:40:09 | 0 | NA | 136 | NA | NA | NA | 0.6 | NA | NA | NA | 0.3 | NA | NA | 105 | 10.7 | NA | NA | NA | 11.9 | 65.8 | NA | NA | NA | NA | 91.8 | 39.2 | 3.54 | NA | 347 | NA | NA | NA | 0.8 | NA | 4.27 | 0.02 | NA | NA | NA | 2.33 | NA | 11.9 | NA | 40.8 | NA | 22.6 | NA | NA | NA | NA | NA | NA | NA | NA | NA | 39.9 | NA | NA | 0.38 | 16.3 | NA | NA | NA | 0.01 | NA | 31.9 | NA | NA | NA | NA | 0.12 | NA | NA | NA | NA |
| NA | 2020-01-31 01:44:00 | 73 | 1 | 2020-01-30 22:12:47 | 2020-02-17 12:40:09 | 0 | NA | NA | 103.1 | NA | NA | NA | NA | 46 | 33.3 | NA | NA | 8.3 | NA | NA | NA | NA | 4.3 | NA | NA | 69.3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 8.5 | NA | NA | NA | NA | 2.29 | 4.33 | NA | NA | 4 | NA | NA | NA | NA | NA | NA | NA | 3.9 | 33 | 418 | 21.2 | 2.02 | NA | 306 | NA | NA | NA | NA | NA | 36 | 24 | NA | NA | NA | NA | NA | 43.1 | NA | 137.7 | NA | NA | 16 | 46.6 | 130 |
| NA | 2020-01-31 01:45:00 | 73 | 1 | 2020-01-30 22:12:47 | 2020-02-17 12:40:09 | 0 | NA | NA | NA | 13.9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 91 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2.2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1.06 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| NA | 2020-01-31 01:56:00 | 73 | 1 | 2020-01-30 22:12:47 | 2020-02-17 12:40:09 | 0 | 19.9 | NA | NA | NA | 0.09 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 7.35 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 60 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Ponieważ mogą wystąpić pewne problemy z kodowaniem, wykorzystano funkcj ‘make.names’, aby zapewnić poprawne nazwy atrybutów
orig_data_df <- dplyr::as_tibble(orig_data_csv) %>%
rename_with(~make.names(.x, unique = T), unique = TRUE)Uzupełnienie identyfikatora
Ponieważ w poprzedniej podsekcji stwierdzono brakujące identyfikatory w wierszach przynależących do konkretnego pacjenta, uzupełniono je za pomocą funkcji ‘fill’ pakietu ‘tidyr’.
new_data_df <- orig_data_df %>% fill(PATIENT_ID) %>% filter(!is.na(RE_DATE))
group_by_id <- new_data_df %>% group_by(PATIENT_ID)Analiza danych
Analiza wieku pacjentów
Poniższy wykres przedstawia rozkład wieku pacjentów.
age_vector <- group_by_id %>% distinct(age) %>% ungroup()
hist_age <- ggplot(data=age_vector, aes(x=age)) +
geom_histogram(binwidth=1, stat = 'bin', fill='steelblue', color='black') +
labs(x='Age', y='Count', title='Age histogram') +
scale_x_continuous(n.breaks = 20) +
scale_y_continuous(n.breaks = 10) +
theme_classic()
ggplotly(hist_age)Zakres wieku jest duży - od 18 do 95 lat. Dlatego można podzielić ten rozkład na 3 grupy - ludzi młodych(Young), w wieku średnim(Mid) oraz starych (Old).
hist_age_grouped <- ggplot(data=age_vector, aes(x=age)) +
geom_histogram(breaks=c(age_min, mid_start, old_start, age_max), stat = 'bin', fill='steelblue', color='black') +
labs(x='Age', y='Count', title='Age histogram by groups.', position='dodge2') +
scale_y_continuous(n.breaks = 10) +
scale_x_continuous(breaks=c(age_min, mid_start, old_start, age_max)) +
theme_classic() +
annotate(geom='text', x=(mid_start+age_min)/2, y=10, label='Young') +
annotate(geom='text', x=(old_start+mid_start)/2, y=10, label='Mid') +
annotate(geom='text', x=(age_max+old_start)/2, y=10, label='Old')
ggplotly(hist_age_grouped)Zauważalne jest, że liczba osób młodych do reszty jest mała. Można przypuszczać, że osoby młode albo są bardziej odporne albo ich stan zdrowotny nie pogorszył się w takim stopniu, aby musieli zostać umieszczeni w szpitalu.
Stosunek liczby ludzi młodych do wszystkich pacjentów: 18.01%
Stosunek liczby ludzi w średnim wieku do wszystkich pacjentów: 41.27%
Stosunek liczby ludzi starych do wszystkich pacjentów: 40.72%
Analiza płci pacjentów
gender_vector <- group_by_id %>% distinct(gender) %>% ungroup()
hist_gender <- ggplot(data=gender_vector, aes(x=gender)) +
geom_histogram(binwidth=1, stat = 'bin', fill='steelblue', color='black') +
labs(x='Gender', y='Count', title='Gender histogram') +
scale_x_continuous(breaks = c(1, 2), label=c('Male', 'Female')) +
theme_classic()
ggplotly(hist_gender)Stosunek liczby mężczyzn do wszystkich pacjentów: 58.73%
Stosunek liczby kobiet do wszystkich pacjentów: 41.27%
Analiza stanu (outcome) pacjentów.
outcome_vector <- group_by_id %>% distinct(outcome) %>% ungroup()
hist_outcome <- ggplot(data=outcome_vector, aes(x=outcome)) +
geom_histogram(binwidth=1, stat = 'bin', fill='steelblue', color='black') +
labs(x='Outcome', y='Count', title='Outcome histogram') +
scale_x_continuous(breaks = c(0, 1), label=c('Survived ', 'Died')) +
theme_classic()
ggplotly(hist_outcome)Stosunek pacjentów, którzy przeżyli do wszystkich pacjentów: 54.02%
Stosunek pacjentów, którzy nie przeżyli do wszystkich pacjentów: 45.98%
Analiza próbek krwii
Wypełnienie wartości pustych
Wartości przed wypełnieniem
Z poniższej tabeli można zauważyć problem z dużą liczbą komórek o wartości NaN.
before_fill <- new_data_df[, c(1, 8:ncol(new_data_df))]
kable(summary(before_fill[, 8:ncol(before_fill)])) %>%
kable_styling(bootstrap_options = "basic",
full_width = F) %>%
scroll_box(width = "100%", height = "100%")| Interleukin.2.receptor | Alkaline.phosphatase | albumin | basophil… | Interleukin.10 | Total.bilirubin | Platelet.count | monocytes… | antithrombin | Interleukin.8 | indirect.bilirubin | Red.blood.cell.distribution.width | neutrophils… | total.protein | Quantification.of.Treponema.pallidum.antibodies | Prothrombin.activity | HBsAg | mean.corpuscular.volume | hematocrit | White.blood.cell.count | Tumor.necrosis.factor.U.03B1. | mean.corpuscular.hemoglobin.concentration | fibrinogen | Interleukin.1ß | Urea | lymphocyte.count | PH.value | Red.blood.cell.count | Eosinophil.count | Corrected.calcium | Serum.potassium | glucose | neutrophils.count | Direct.bilirubin | Mean.platelet.volume | ferritin | RBC.distribution.width.SD | Thrombin.time | X…lymphocyte | HCV.antibody.quantification | D.D.dimer | Total.cholesterol | aspartate.aminotransferase | Uric.acid | HCO3. | calcium | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Lactate.dehydrogenase | platelet.large.cell.ratio | Interleukin.6 | Fibrin.degradation.products | monocytes.count | PLT.distribution.width | globulin | X.U.03B3..glutamyl.transpeptidase | International.standard.ratio | basophil.count… | X2019.nCoV.nucleic.acid.detection | mean.corpuscular.hemoglobin | Activation.of.partial.thromboplastin.time | High.sensitivity.C.reactive.protein | HIV.antibody.quantification | serum.sodium | thrombocytocrit | ESR | glutamic.pyruvic.transaminase | eGFR | creatinine | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 61.0 | Min. : 17.00 | Min. :13.60 | Min. :0.00 | Min. : 5.00 | Min. : 2.50 | Min. : -1.0 | Min. : 0.300 | Min. : 20.00 | Min. : 5.000 | Min. : 0.100 | Min. :10.60 | Min. : 1.7 | Min. :31.80 | Min. : 0.020 | Min. : 6.00 | Min. : 0.000 | Min. : 61.60 | Min. :14.50 | Min. : 0.13 | Min. : 4.00 | Min. :286.0 | Min. : 0.500 | Min. : 5.00 | Min. : 0.800 | Min. : 0.000 | Min. :5.000 | Min. : 0.100 | Min. :0.000 | Min. :1.650 | Min. : 2.760 | Min. : 1.000 | Min. : 0.06 | Min. : 1.600 | Min. : 8.50 | Min. : 17.8 | Min. : 31.30 | Min. : 13.00 | Min. : 0.000 | Min. :0.020 | Min. : 0.210 | Min. :0.100 | Min. : 6.00 | Min. : 43.0 | Min. : 6.30 | Min. :1.170 | Min. : 5 | Min. : 110.0 | Min. :11.20 | Min. : 1.500 | Min. : 4.00 | Min. : 0.010 | Min. : 8.00 | Min. :10.10 | Min. : 3.00 | Min. : 0.840 | Min. :0.000 | Min. :-1 | Min. :20.4 | Min. : 21.80 | Min. : 0.10 | Min. :0.05 | Min. :115.4 | Min. :0.010 | Min. : 1.00 | Min. : 5.00 | Min. : 2.00 | Min. : 11.00 | |
| 1st Qu.: 459.5 | 1st Qu.: 54.00 | 1st Qu.:27.40 | 1st Qu.:0.10 | 1st Qu.: 5.00 | 1st Qu.: 7.40 | 1st Qu.:109.0 | 1st Qu.: 2.800 | 1st Qu.: 74.00 | 1st Qu.: 8.675 | 1st Qu.: 3.800 | 1st Qu.:12.00 | 1st Qu.:65.1 | 1st Qu.:61.00 | 1st Qu.: 0.040 | 1st Qu.: 65.00 | 1st Qu.: 0.000 | 1st Qu.: 86.90 | 1st Qu.:33.50 | 1st Qu.: 4.94 | 1st Qu.: 6.70 | 1st Qu.:333.0 | 1st Qu.: 3.050 | 1st Qu.: 5.00 | 1st Qu.: 4.000 | 1st Qu.: 0.460 | 1st Qu.:6.000 | 1st Qu.: 3.680 | 1st Qu.:0.000 | 1st Qu.:2.270 | 1st Qu.: 3.950 | 1st Qu.: 5.550 | 1st Qu.: 3.09 | 1st Qu.: 3.225 | 1st Qu.:10.10 | 1st Qu.: 377.2 | 1st Qu.: 38.50 | 1st Qu.: 15.60 | 1st Qu.: 3.925 | 1st Qu.:0.040 | 1st Qu.: 0.603 | 1st Qu.:3.010 | 1st Qu.: 19.50 | 1st Qu.: 183.2 | 1st Qu.:21.00 | 1st Qu.:1.980 | 1st Qu.: 150 | 1st Qu.: 218.0 | 1st Qu.:25.60 | 1st Qu.: 4.772 | 1st Qu.: 4.00 | 1st Qu.: 0.270 | 1st Qu.:11.10 | 1st Qu.:29.70 | 1st Qu.: 22.00 | 1st Qu.: 1.030 | 1st Qu.:0.010 | 1st Qu.:-1 | 1st Qu.:29.7 | 1st Qu.: 35.30 | 1st Qu.: 5.70 | 1st Qu.:0.07 | 1st Qu.:137.7 | 1st Qu.:0.150 | 1st Qu.: 14.00 | 1st Qu.: 16.00 | 1st Qu.: 63.58 | 1st Qu.: 58.00 | |
| Median : 676.5 | Median : 69.50 | Median :32.20 | Median :0.20 | Median : 5.90 | Median : 10.70 | Median :178.0 | Median : 5.700 | Median : 86.00 | Median : 16.000 | Median : 5.400 | Median :12.60 | Median :82.4 | Median :65.90 | Median : 0.050 | Median : 81.00 | Median : 0.010 | Median : 90.10 | Median :36.60 | Median : 7.72 | Median : 8.60 | Median :343.0 | Median : 4.120 | Median : 5.00 | Median : 5.985 | Median : 0.800 | Median :6.500 | Median : 4.140 | Median :0.010 | Median :2.360 | Median : 4.410 | Median : 6.990 | Median : 5.85 | Median : 4.800 | Median :10.80 | Median : 711.0 | Median : 40.90 | Median : 16.80 | Median :11.450 | Median :0.060 | Median : 2.155 | Median :3.630 | Median : 27.00 | Median : 243.7 | Median :23.50 | Median :2.080 | Median : 585 | Median : 340.0 | Median :30.90 | Median : 19.265 | Median : 17.90 | Median : 0.410 | Median :12.40 | Median :32.70 | Median : 34.00 | Median : 1.140 | Median :0.010 | Median :-1 | Median :30.9 | Median : 39.20 | Median : 51.50 | Median :0.09 | Median :140.4 | Median :0.210 | Median : 28.00 | Median : 24.00 | Median : 87.90 | Median : 76.00 | |
| Mean : 907.2 | Mean : 82.47 | Mean :32.01 | Mean :0.21 | Mean : 16.07 | Mean : 16.70 | Mean :184.3 | Mean : 6.155 | Mean : 85.32 | Mean : 83.088 | Mean : 6.889 | Mean :13.07 | Mean :77.6 | Mean :65.30 | Mean : 0.132 | Mean : 78.55 | Mean : 8.306 | Mean : 90.39 | Mean :36.55 | Mean : 15.60 | Mean : 11.58 | Mean :342.8 | Mean : 4.294 | Mean : 6.51 | Mean : 9.589 | Mean : 1.017 | Mean :6.484 | Mean : 9.288 | Mean :0.039 | Mean :2.355 | Mean : 4.509 | Mean : 8.889 | Mean : 7.81 | Mean : 9.887 | Mean :10.91 | Mean : 1379.1 | Mean : 42.44 | Mean : 18.17 | Mean :15.392 | Mean :0.117 | Mean : 7.943 | Mean :3.689 | Mean : 46.53 | Mean : 276.1 | Mean :23.14 | Mean :2.078 | Mean : 3669 | Mean : 474.2 | Mean :31.77 | Mean : 112.308 | Mean : 61.35 | Mean : 0.526 | Mean :13.01 | Mean :33.24 | Mean : 55.34 | Mean : 1.313 | Mean :0.017 | Mean :-1 | Mean :31.0 | Mean : 41.52 | Mean : 76.24 | Mean :0.10 | Mean :141.6 | Mean :0.212 | Mean : 33.69 | Mean : 38.86 | Mean : 81.56 | Mean : 109.93 | |
| 3rd Qu.:1155.5 | 3rd Qu.: 95.00 | 3rd Qu.:36.60 | 3rd Qu.:0.30 | 3rd Qu.: 12.35 | 3rd Qu.: 16.77 | 3rd Qu.:248.0 | 3rd Qu.: 8.600 | 3rd Qu.: 97.00 | 3rd Qu.: 35.200 | 3rd Qu.: 8.000 | 3rd Qu.:13.70 | 3rd Qu.:92.3 | 3rd Qu.:70.45 | 3rd Qu.: 0.070 | 3rd Qu.: 95.00 | 3rd Qu.: 0.010 | 3rd Qu.: 93.90 | 3rd Qu.:39.90 | 3rd Qu.: 12.72 | 3rd Qu.: 11.50 | 3rd Qu.:350.0 | 3rd Qu.: 5.480 | 3rd Qu.: 5.00 | 3rd Qu.:11.400 | 3rd Qu.: 1.310 | 3rd Qu.:7.294 | 3rd Qu.: 4.650 | 3rd Qu.:0.060 | 3rd Qu.:2.440 | 3rd Qu.: 4.870 | 3rd Qu.:10.260 | 3rd Qu.:10.95 | 3rd Qu.: 8.275 | 3rd Qu.:11.50 | 3rd Qu.: 1425.2 | 3rd Qu.: 44.70 | 3rd Qu.: 18.38 | 3rd Qu.:24.975 | 3rd Qu.:0.090 | 3rd Qu.:21.000 | 3rd Qu.:4.265 | 3rd Qu.: 42.00 | 3rd Qu.: 333.8 | 3rd Qu.:25.90 | 3rd Qu.:2.190 | 3rd Qu.: 2625 | 3rd Qu.: 601.8 | 3rd Qu.:37.20 | 3rd Qu.: 60.167 | 3rd Qu.:150.00 | 3rd Qu.: 0.580 | 3rd Qu.:14.30 | 3rd Qu.:36.50 | 3rd Qu.: 58.00 | 3rd Qu.: 1.330 | 3rd Qu.:0.020 | 3rd Qu.:-1 | 3rd Qu.:32.2 | 3rd Qu.: 44.12 | 3rd Qu.:118.50 | 3rd Qu.:0.11 | 3rd Qu.:143.5 | 3rd Qu.:0.270 | 3rd Qu.: 45.50 | 3rd Qu.: 41.00 | 3rd Qu.:103.97 | 3rd Qu.: 98.25 | |
| Max. :7500.0 | Max. :620.00 | Max. :48.60 | Max. :1.70 | Max. :1000.00 | Max. :505.70 | Max. :558.0 | Max. :53.000 | Max. :136.00 | Max. :6795.000 | Max. :145.100 | Max. :27.10 | Max. :98.9 | Max. :88.70 | Max. :11.950 | Max. :142.00 | Max. :250.000 | Max. :118.90 | Max. :52.30 | Max. :1726.60 | Max. :168.00 | Max. :514.0 | Max. :10.780 | Max. :88.50 | Max. :68.400 | Max. :52.420 | Max. :7.565 | Max. :749.500 | Max. :0.490 | Max. :2.790 | Max. :12.800 | Max. :43.010 | Max. :33.88 | Max. :360.600 | Max. :15.00 | Max. :50000.0 | Max. :113.30 | Max. :161.90 | Max. :60.000 | Max. :2.090 | Max. :60.000 | Max. :7.300 | Max. :1858.00 | Max. :1176.0 | Max. :36.30 | Max. :2.620 | Max. :70000 | Max. :1867.0 | Max. :62.20 | Max. :5000.000 | Max. :190.80 | Max. :39.920 | Max. :25.30 | Max. :50.60 | Max. :732.00 | Max. :13.480 | Max. :0.120 | Max. :-1 | Max. :50.8 | Max. :144.00 | Max. :320.00 | Max. :0.27 | Max. :179.7 | Max. :0.510 | Max. :110.00 | Max. :1600.00 | Max. :224.00 | Max. :1497.00 | |
| NA’s :5838 | NA’s :5176 | NA’s :5172 | NA’s :5149 | NA’s :5839 | NA’s :5176 | NA’s :5149 | NA’s :5148 | NA’s :5776 | NA’s :5838 | NA’s :5200 | NA’s :5183 | NA’s :5149 | NA’s :5175 | NA’s :5827 | NA’s :5447 | NA’s :5827 | NA’s :5149 | NA’s :5149 | NA’s :4979 | NA’s :5838 | NA’s :5149 | NA’s :5540 | NA’s :5838 | NA’s :5170 | NA’s :5149 | NA’s :5722 | NA’s :4979 | NA’s :5149 | NA’s :5192 | NA’s :5126 | NA’s :5331 | NA’s :5149 | NA’s :5176 | NA’s :5244 | NA’s :5823 | NA’s :5183 | NA’s :5540 | NA’s :5148 | NA’s :5827 | NA’s :5476 | NA’s :5175 | NA’s :5171 | NA’s :5172 | NA’s :5172 | NA’s :5127 | NA’s :5631 | NA’s :5172 | NA’s :5244 | NA’s :5834 | NA’s :5776 | NA’s :5149 | NA’s :5244 | NA’s :5176 | NA’s :5176 | NA’s :5447 | NA’s :5149 | NA’s :5605 | NA’s :5149 | NA’s :5538 | NA’s :5369 | NA’s :5828 | NA’s :5131 | NA’s :5244 | NA’s :5723 | NA’s :5175 | NA’s :5170 | NA’s :5170 |
Stosunek dla najmniejszej liczby wystąpień wartości NaN do całkowitej liczby wierszy przed wypełnieniem 81.5427448%
Stosunek dla największej liczby wystąpień wartości NaN do całkowitej liczby wierszy przed wypełnieniem 95.6272519%
Wartości po wypełnieniu
Do wypełnienia danych z tych komórek wykorzystano funkcję ‘fill’, która najpierw wypełni komórki w dół, gdy napotka wartość inną niż NaN, a następnie wypełni w górę.
after_fill <- before_fill %>%
group_by(PATIENT_ID) %>%
fill(names(.), .direction = 'downup') %>%
ungroup()
kable(summary(after_fill[, 2:ncol(after_fill)])) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "100%")| Hypersensitive.cardiac.troponinI | hemoglobin | Serum.chloride | Prothrombin.time | procalcitonin | eosinophils… | Interleukin.2.receptor | Alkaline.phosphatase | albumin | basophil… | Interleukin.10 | Total.bilirubin | Platelet.count | monocytes… | antithrombin | Interleukin.8 | indirect.bilirubin | Red.blood.cell.distribution.width | neutrophils… | total.protein | Quantification.of.Treponema.pallidum.antibodies | Prothrombin.activity | HBsAg | mean.corpuscular.volume | hematocrit | White.blood.cell.count | Tumor.necrosis.factor.U.03B1. | mean.corpuscular.hemoglobin.concentration | fibrinogen | Interleukin.1ß | Urea | lymphocyte.count | PH.value | Red.blood.cell.count | Eosinophil.count | Corrected.calcium | Serum.potassium | glucose | neutrophils.count | Direct.bilirubin | Mean.platelet.volume | ferritin | RBC.distribution.width.SD | Thrombin.time | X…lymphocyte | HCV.antibody.quantification | D.D.dimer | Total.cholesterol | aspartate.aminotransferase | Uric.acid | HCO3. | calcium | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Lactate.dehydrogenase | platelet.large.cell.ratio | Interleukin.6 | Fibrin.degradation.products | monocytes.count | PLT.distribution.width | globulin | X.U.03B3..glutamyl.transpeptidase | International.standard.ratio | basophil.count… | X2019.nCoV.nucleic.acid.detection | mean.corpuscular.hemoglobin | Activation.of.partial.thromboplastin.time | High.sensitivity.C.reactive.protein | HIV.antibody.quantification | serum.sodium | thrombocytocrit | ESR | glutamic.pyruvic.transaminase | eGFR | creatinine | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.9 | Min. : 6.4 | Min. : 71.5 | Min. : 11.50 | Min. : 0.0200 | Min. :0.0000 | Min. : 61.0 | Min. : 17.00 | Min. :13.60 | Min. :0.0000 | Min. : 5.00 | Min. : 2.50 | Min. : -1.0 | Min. : 0.300 | Min. : 20.00 | Min. : 5.00 | Min. : 0.100 | Min. :10.6 | Min. : 1.7 | Min. :31.80 | Min. : 0.0200 | Min. : 6.00 | Min. : 0.000 | Min. : 61.60 | Min. :14.50 | Min. : 0.13 | Min. : 4.00 | Min. :286.0 | Min. : 0.500 | Min. : 5.000 | Min. : 0.800 | Min. : 0.0000 | Min. :5.000 | Min. : 0.100 | Min. :0.0000 | Min. :1.650 | Min. : 2.760 | Min. : 1.000 | Min. : 0.06 | Min. : 1.600 | Min. : 8.50 | Min. : 17.8 | Min. : 31.30 | Min. : 13.00 | Min. : 0.00 | Min. :0.0200 | Min. : 0.210 | Min. :0.100 | Min. : 6.00 | Min. : 43.0 | Min. : 6.30 | Min. :1.170 | Min. : 5 | Min. : 110.0 | Min. :11.20 | Min. : 1.50 | Min. : 4.00 | Min. : 0.0100 | Min. : 8.0 | Min. :10.1 | Min. : 3.00 | Min. : 0.840 | Min. :0.00000 | Min. :-1 | Min. :20.40 | Min. : 21.80 | Min. : 0.10 | Min. :0.0500 | Min. :115.4 | Min. :0.0100 | Min. : 1.00 | Min. : 5.00 | Min. : 2.00 | Min. : 11.00 | |
| 1st Qu.: 2.7 | 1st Qu.:114.0 | 1st Qu.: 98.8 | 1st Qu.: 13.50 | 1st Qu.: 0.0400 | 1st Qu.:0.0000 | 1st Qu.: 513.0 | 1st Qu.: 54.00 | 1st Qu.:28.40 | 1st Qu.:0.1000 | 1st Qu.: 5.00 | 1st Qu.: 7.20 | 1st Qu.:121.0 | 1st Qu.: 3.100 | 1st Qu.: 77.00 | 1st Qu.: 9.90 | 1st Qu.: 3.600 | 1st Qu.:11.9 | 1st Qu.:65.1 | 1st Qu.:62.20 | 1st Qu.: 0.0400 | 1st Qu.: 70.00 | 1st Qu.: 0.000 | 1st Qu.: 86.80 | 1st Qu.:33.80 | 1st Qu.: 4.83 | 1st Qu.: 7.00 | 1st Qu.:334.0 | 1st Qu.: 3.260 | 1st Qu.: 5.000 | 1st Qu.: 3.820 | 1st Qu.: 0.4900 | 1st Qu.:6.000 | 1st Qu.: 3.700 | 1st Qu.:0.0000 | 1st Qu.:2.270 | 1st Qu.: 3.920 | 1st Qu.: 5.630 | 1st Qu.: 2.97 | 1st Qu.: 3.200 | 1st Qu.:10.20 | 1st Qu.: 422.6 | 1st Qu.: 38.30 | 1st Qu.: 15.70 | 1st Qu.: 4.40 | 1st Qu.:0.0400 | 1st Qu.: 0.510 | 1st Qu.:2.970 | 1st Qu.: 20.00 | 1st Qu.: 184.2 | 1st Qu.:21.00 | 1st Qu.:2.000 | 1st Qu.: 72 | 1st Qu.: 225.0 | 1st Qu.:26.40 | 1st Qu.: 7.67 | 1st Qu.: 4.00 | 1st Qu.: 0.2800 | 1st Qu.:11.3 | 1st Qu.:30.1 | 1st Qu.: 21.00 | 1st Qu.: 1.030 | 1st Qu.:0.01000 | 1st Qu.:-1 | 1st Qu.:29.70 | 1st Qu.: 35.60 | 1st Qu.: 8.60 | 1st Qu.:0.0700 | 1st Qu.:137.2 | 1st Qu.:0.1400 | 1st Qu.: 15.00 | 1st Qu.: 15.00 | 1st Qu.: 66.70 | 1st Qu.: 58.00 | |
| Median : 12.9 | Median :126.0 | Median :101.7 | Median : 14.30 | Median : 0.1000 | Median :0.1000 | Median : 778.0 | Median : 68.00 | Median :33.05 | Median :0.2000 | Median : 7.50 | Median : 10.30 | Median :180.0 | Median : 6.000 | Median : 88.00 | Median : 17.10 | Median : 5.300 | Median :12.5 | Median :80.9 | Median :66.70 | Median : 0.0500 | Median : 86.00 | Median : 0.010 | Median : 89.80 | Median :36.90 | Median : 7.33 | Median : 8.70 | Median :343.0 | Median : 4.410 | Median : 5.000 | Median : 5.600 | Median : 0.7900 | Median :6.500 | Median : 4.160 | Median :0.0100 | Median :2.360 | Median : 4.330 | Median : 6.960 | Median : 5.42 | Median : 4.600 | Median :10.80 | Median : 826.8 | Median : 40.60 | Median : 16.70 | Median :12.40 | Median :0.0600 | Median : 1.350 | Median :3.590 | Median : 28.50 | Median : 243.4 | Median :23.20 | Median :2.100 | Median : 332 | Median : 338.0 | Median :31.40 | Median : 25.36 | Median : 7.40 | Median : 0.4000 | Median :12.6 | Median :33.1 | Median : 33.00 | Median : 1.100 | Median :0.01000 | Median :-1 | Median :30.90 | Median : 39.40 | Median : 51.90 | Median :0.0900 | Median :140.1 | Median :0.2000 | Median : 31.00 | Median : 23.00 | Median : 89.20 | Median : 76.00 | |
| Mean : 958.5 | Mean :125.1 | Mean :102.3 | Mean : 15.52 | Mean : 0.7299 | Mean :0.5679 | Mean : 968.3 | Mean : 80.78 | Mean :32.72 | Mean :0.2012 | Mean : 18.49 | Mean : 15.77 | Mean :187.6 | Mean : 6.359 | Mean : 88.17 | Mean : 52.59 | Mean : 6.801 | Mean :13.0 | Mean :77.1 | Mean :66.23 | Mean : 0.1671 | Mean : 82.58 | Mean : 6.021 | Mean : 90.04 | Mean :36.78 | Mean : 12.42 | Mean : 11.59 | Mean :343.4 | Mean : 4.486 | Mean : 6.364 | Mean : 8.374 | Mean : 0.9754 | Mean :6.410 | Mean : 7.774 | Mean :0.0342 | Mean :2.351 | Mean : 4.408 | Mean : 8.665 | Mean : 7.44 | Mean : 9.006 | Mean :10.98 | Mean : 1486.2 | Mean : 42.07 | Mean : 17.91 | Mean :15.75 | Mean :0.1052 | Mean : 6.434 | Mean :3.651 | Mean : 41.96 | Mean : 271.6 | Mean :23.01 | Mean :2.094 | Mean : 2332 | Mean : 453.6 | Mean :32.34 | Mean : 93.75 | Mean : 53.85 | Mean : 0.4899 | Mean :13.2 | Mean :33.5 | Mean : 54.88 | Mean : 1.236 | Mean :0.01596 | Mean :-1 | Mean :30.93 | Mean : 40.75 | Mean : 76.05 | Mean :0.0956 | Mean :140.6 | Mean :0.2065 | Mean : 35.12 | Mean : 34.56 | Mean : 83.71 | Mean : 99.22 | |
| 3rd Qu.: 59.4 | 3rd Qu.:138.0 | 3rd Qu.:104.6 | 3rd Qu.: 15.90 | 3rd Qu.: 0.3500 | 3rd Qu.:0.7000 | 3rd Qu.:1190.0 | 3rd Qu.: 91.00 | 3rd Qu.:37.40 | 3rd Qu.:0.3000 | 3rd Qu.: 14.00 | 3rd Qu.: 15.40 | 3rd Qu.:245.0 | 3rd Qu.: 8.800 | 3rd Qu.: 98.00 | 3rd Qu.: 38.60 | 3rd Qu.: 7.700 | 3rd Qu.:13.5 | 3rd Qu.:91.6 | 3rd Qu.:70.90 | 3rd Qu.: 0.0700 | 3rd Qu.: 96.00 | 3rd Qu.: 0.010 | 3rd Qu.: 93.40 | 3rd Qu.:40.10 | 3rd Qu.: 12.18 | 3rd Qu.: 12.10 | 3rd Qu.:351.0 | 3rd Qu.: 5.620 | 3rd Qu.: 5.000 | 3rd Qu.: 9.700 | 3rd Qu.: 1.2800 | 3rd Qu.:7.000 | 3rd Qu.: 4.610 | 3rd Qu.:0.0500 | 3rd Qu.:2.430 | 3rd Qu.: 4.780 | 3rd Qu.: 9.870 | 3rd Qu.:10.46 | 3rd Qu.: 7.500 | 3rd Qu.:11.60 | 3rd Qu.: 1519.9 | 3rd Qu.: 44.10 | 3rd Qu.: 18.10 | 3rd Qu.:24.70 | 3rd Qu.:0.0900 | 3rd Qu.:12.050 | 3rd Qu.:4.220 | 3rd Qu.: 43.00 | 3rd Qu.: 329.0 | 3rd Qu.:25.50 | 3rd Qu.:2.190 | 3rd Qu.: 1180 | 3rd Qu.: 576.0 | 3rd Qu.:37.80 | 3rd Qu.: 71.96 | 3rd Qu.:150.00 | 3rd Qu.: 0.5800 | 3rd Qu.:14.7 | 3rd Qu.:36.6 | 3rd Qu.: 57.00 | 3rd Qu.: 1.260 | 3rd Qu.:0.02000 | 3rd Qu.:-1 | 3rd Qu.:32.10 | 3rd Qu.: 44.10 | 3rd Qu.:118.30 | 3rd Qu.:0.1000 | 3rd Qu.:142.7 | 3rd Qu.:0.2600 | 3rd Qu.: 47.00 | 3rd Qu.: 38.00 | 3rd Qu.:105.00 | 3rd Qu.: 97.00 | |
| Max. :50000.0 | Max. :178.0 | Max. :140.4 | Max. :120.00 | Max. :57.1700 | Max. :8.6000 | Max. :7500.0 | Max. :620.00 | Max. :48.60 | Max. :1.7000 | Max. :1000.00 | Max. :505.70 | Max. :558.0 | Max. :53.000 | Max. :136.00 | Max. :6795.00 | Max. :145.100 | Max. :27.1 | Max. :98.9 | Max. :88.70 | Max. :11.9500 | Max. :142.00 | Max. :250.000 | Max. :118.90 | Max. :52.30 | Max. :1726.60 | Max. :168.00 | Max. :514.0 | Max. :10.780 | Max. :88.500 | Max. :68.400 | Max. :52.4200 | Max. :7.565 | Max. :749.500 | Max. :0.4900 | Max. :2.790 | Max. :12.800 | Max. :43.010 | Max. :33.88 | Max. :360.600 | Max. :15.00 | Max. :50000.0 | Max. :113.30 | Max. :161.90 | Max. :60.00 | Max. :2.0900 | Max. :60.000 | Max. :7.300 | Max. :1858.00 | Max. :1176.0 | Max. :36.30 | Max. :2.620 | Max. :70000 | Max. :1867.0 | Max. :62.20 | Max. :5000.00 | Max. :190.80 | Max. :39.9200 | Max. :25.3 | Max. :50.6 | Max. :732.00 | Max. :13.480 | Max. :0.12000 | Max. :-1 | Max. :50.80 | Max. :144.00 | Max. :320.00 | Max. :0.2700 | Max. :179.7 | Max. :0.5100 | Max. :110.00 | Max. :1600.00 | Max. :224.00 | Max. :1497.00 | |
| NA’s :1008 | NA’s :20 | NA’s :32 | NA’s :65 | NA’s :460 | NA’s :20 | NA’s :1857 | NA’s :18 | NA’s :18 | NA’s :20 | NA’s :1870 | NA’s :18 | NA’s :20 | NA’s :20 | NA’s :2266 | NA’s :1857 | NA’s :35 | NA’s :100 | NA’s :20 | NA’s :18 | NA’s :1127 | NA’s :65 | NA’s :1117 | NA’s :20 | NA’s :20 | NA’s :16 | NA’s :1857 | NA’s :20 | NA’s :752 | NA’s :1857 | NA’s :18 | NA’s :20 | NA’s :1657 | NA’s :16 | NA’s :20 | NA’s :43 | NA’s :32 | NA’s :67 | NA’s :20 | NA’s :18 | NA’s :110 | NA’s :1817 | NA’s :100 | NA’s :752 | NA’s :20 | NA’s :1117 | NA’s :150 | NA’s :18 | NA’s :18 | NA’s :18 | NA’s :18 | NA’s :32 | NA’s :1247 | NA’s :18 | NA’s :110 | NA’s :1832 | NA’s :2266 | NA’s :20 | NA’s :110 | NA’s :18 | NA’s :18 | NA’s :65 | NA’s :20 | NA’s :2136 | NA’s :20 | NA’s :752 | NA’s :62 | NA’s :1147 | NA’s :32 | NA’s :110 | NA’s :835 | NA’s :18 | NA’s :18 | NA’s :18 |
Stosunek dla najmniejszej liczby wystąpień wartości NaN do całkowitej liczby wierszy po wypełnieniu 0.2620373%
Stosunek dla największej liczby wystąpień wartości NaN do całkowitej liczby wierszy po wypełnieniu 37.1110383%
Dzięki temu zabiegowi uzyskano znaczny spadek liczby komórek zawierających NaN. ### Wybranie konkretnych próbek Ponieważ kilka wierszy przynależy do jednego pacjenta, to dka każdego pacjenta wybrano mediane wartości z każdej kolumny. Dzięki temu uzyska się po jednym wierszy na pacjenta.
data_fill_summarise <- after_fill %>%
group_by(PATIENT_ID) %>%
summarise(across(everything(),
~median(.x, na.rm = TRUE),
.names = "{.col}"))## `summarise()` ungrouping output (override with `.groups` argument)kable(summary(data_fill_summarise[,-1])) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "100%")| Hypersensitive.cardiac.troponinI | hemoglobin | Serum.chloride | Prothrombin.time | procalcitonin | eosinophils… | Interleukin.2.receptor | Alkaline.phosphatase | albumin | basophil… | Interleukin.10 | Total.bilirubin | Platelet.count | monocytes… | antithrombin | Interleukin.8 | indirect.bilirubin | Red.blood.cell.distribution.width | neutrophils… | total.protein | Quantification.of.Treponema.pallidum.antibodies | Prothrombin.activity | HBsAg | mean.corpuscular.volume | hematocrit | White.blood.cell.count | Tumor.necrosis.factor.U.03B1. | mean.corpuscular.hemoglobin.concentration | fibrinogen | Interleukin.1ß | Urea | lymphocyte.count | PH.value | Red.blood.cell.count | Eosinophil.count | Corrected.calcium | Serum.potassium | glucose | neutrophils.count | Direct.bilirubin | Mean.platelet.volume | ferritin | RBC.distribution.width.SD | Thrombin.time | X…lymphocyte | HCV.antibody.quantification | D.D.dimer | Total.cholesterol | aspartate.aminotransferase | Uric.acid | HCO3. | calcium | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Lactate.dehydrogenase | platelet.large.cell.ratio | Interleukin.6 | Fibrin.degradation.products | monocytes.count | PLT.distribution.width | globulin | X.U.03B3..glutamyl.transpeptidase | International.standard.ratio | basophil.count… | X2019.nCoV.nucleic.acid.detection | mean.corpuscular.hemoglobin | Activation.of.partial.thromboplastin.time | High.sensitivity.C.reactive.protein | HIV.antibody.quantification | serum.sodium | thrombocytocrit | ESR | glutamic.pyruvic.transaminase | eGFR | creatinine | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.90 | Min. : 14.1 | Min. : 71.5 | Min. : 11.50 | Min. : 0.0200 | Min. :0.0000 | Min. : 70.0 | Min. : 17.00 | Min. :18.50 | Min. :0.000 | Min. : 5.00 | Min. : 2.500 | Min. : -1.0 | Min. : 0.600 | Min. : 42.00 | Min. : 5.000 | Min. : 1.100 | Min. :10.70 | Min. : 1.70 | Min. :45.20 | Min. : 0.0200 | Min. : 6.00 | Min. : 0.000 | Min. : 61.60 | Min. :17.8 | Min. : 0.800 | Min. : 4.00 | Min. :299.0 | Min. : 0.600 | Min. : 5.000 | Min. : 0.800 | Min. : 0.000 | Min. :5.000 | Min. : 1.60 | Min. :0.00000 | Min. :2.060 | Min. :2.860 | Min. : 1.000 | Min. : 0.320 | Min. : 1.600 | Min. : 8.50 | Min. : 17.8 | Min. :31.30 | Min. : 13.90 | Min. : 0.00 | Min. :0.020 | Min. : 0.210 | Min. :1.340 | Min. : 7.00 | Min. : 81.0 | Min. : 9.80 | Min. :1.780 | Min. : 5.0 | Min. : 119.0 | Min. :11.20 | Min. : 1.500 | Min. : 4.0 | Min. : 0.0300 | Min. : 8.20 | Min. :18.50 | Min. : 7.00 | Min. : 0.840 | Min. :0.00000 | Min. :-1 | Min. :20.80 | Min. : 21.80 | Min. : 0.1 | Min. :0.0500 | Min. :115.4 | Min. :0.0100 | Min. : 1.0 | Min. : 5.00 | Min. : 2.30 | Min. : 11.00 | |
| 1st Qu.: 2.60 | 1st Qu.:115.0 | 1st Qu.: 98.5 | 1st Qu.: 13.50 | 1st Qu.: 0.0400 | 1st Qu.:0.0000 | 1st Qu.: 460.0 | 1st Qu.: 54.00 | 1st Qu.:29.48 | 1st Qu.:0.100 | 1st Qu.: 5.00 | 1st Qu.: 6.975 | 1st Qu.:129.0 | 1st Qu.: 3.275 | 1st Qu.: 79.25 | 1st Qu.: 9.375 | 1st Qu.: 3.500 | 1st Qu.:11.90 | 1st Qu.:63.70 | 1st Qu.:63.20 | 1st Qu.: 0.0400 | 1st Qu.: 71.00 | 1st Qu.: 0.000 | 1st Qu.: 86.67 | 1st Qu.:33.9 | 1st Qu.: 4.640 | 1st Qu.: 6.90 | 1st Qu.:336.0 | 1st Qu.: 3.402 | 1st Qu.: 5.000 | 1st Qu.: 3.800 | 1st Qu.: 0.530 | 1st Qu.:6.000 | 1st Qu.: 3.81 | 1st Qu.:0.00000 | 1st Qu.:2.260 | 1st Qu.:3.890 | 1st Qu.: 5.650 | 1st Qu.: 2.850 | 1st Qu.: 3.000 | 1st Qu.:10.20 | 1st Qu.: 402.0 | 1st Qu.:38.30 | 1st Qu.: 15.60 | 1st Qu.: 5.00 | 1st Qu.:0.040 | 1st Qu.: 0.485 | 1st Qu.:2.960 | 1st Qu.: 20.00 | 1st Qu.:189.0 | 1st Qu.:20.80 | 1st Qu.:2.013 | 1st Qu.: 67.5 | 1st Qu.: 229.0 | 1st Qu.:26.00 | 1st Qu.: 5.972 | 1st Qu.: 4.0 | 1st Qu.: 0.2900 | 1st Qu.:11.12 | 1st Qu.:30.30 | 1st Qu.: 20.00 | 1st Qu.: 1.030 | 1st Qu.:0.01000 | 1st Qu.:-1 | 1st Qu.:29.70 | 1st Qu.: 35.90 | 1st Qu.: 10.2 | 1st Qu.:0.0700 | 1st Qu.:137.3 | 1st Qu.:0.1500 | 1st Qu.: 14.0 | 1st Qu.: 15.00 | 1st Qu.: 68.61 | 1st Qu.: 58.00 | |
| Median : 11.00 | Median :127.0 | Median :101.5 | Median : 14.30 | Median : 0.1000 | Median :0.1000 | Median : 693.5 | Median : 68.00 | Median :33.45 | Median :0.200 | Median : 6.00 | Median : 9.900 | Median :184.5 | Median : 6.300 | Median : 88.00 | Median : 16.450 | Median : 5.200 | Median :12.45 | Median :77.90 | Median :67.10 | Median : 0.0500 | Median : 87.00 | Median : 0.010 | Median : 89.60 | Median :37.0 | Median : 6.740 | Median : 8.60 | Median :344.0 | Median : 4.580 | Median : 5.000 | Median : 5.275 | Median : 0.820 | Median :6.500 | Median : 4.18 | Median :0.01000 | Median :2.350 | Median :4.230 | Median : 6.920 | Median : 4.945 | Median : 4.600 | Median :10.80 | Median : 711.6 | Median :40.10 | Median : 16.60 | Median :13.55 | Median :0.060 | Median : 1.330 | Median :3.505 | Median : 29.00 | Median :245.0 | Median :22.90 | Median :2.110 | Median : 296.0 | Median : 325.5 | Median :31.00 | Median : 22.125 | Median : 5.1 | Median : 0.4000 | Median :12.57 | Median :33.15 | Median : 31.00 | Median : 1.090 | Median :0.01000 | Median :-1 | Median :30.80 | Median : 39.45 | Median : 52.0 | Median :0.0900 | Median :139.8 | Median :0.2000 | Median : 29.0 | Median : 22.00 | Median : 90.10 | Median : 74.50 | |
| Mean : 702.23 | Mean :126.5 | Mean :101.8 | Mean : 15.32 | Mean : 0.7204 | Mean :0.5447 | Mean : 933.7 | Mean : 80.15 | Mean :33.54 | Mean :0.206 | Mean : 14.91 | Mean : 14.796 | Mean :189.3 | Mean : 6.594 | Mean : 88.77 | Mean : 38.018 | Mean : 6.639 | Mean :12.90 | Mean :75.95 | Mean :66.93 | Mean : 0.1325 | Mean : 83.13 | Mean : 8.427 | Mean : 89.63 | Mean :37.0 | Mean : 8.921 | Mean :11.27 | Mean :344.2 | Mean : 4.578 | Mean : 6.438 | Mean : 7.849 | Mean : 1.083 | Mean :6.440 | Mean : 4.86 | Mean :0.03046 | Mean :2.336 | Mean :4.312 | Mean : 8.278 | Mean : 6.926 | Mean : 8.085 | Mean :10.89 | Mean : 1469.4 | Mean :41.60 | Mean : 17.69 | Mean :16.59 | Mean :0.112 | Mean : 6.245 | Mean :3.602 | Mean : 40.02 | Mean :274.0 | Mean :22.53 | Mean :2.104 | Mean : 2035.3 | Mean : 445.1 | Mean :31.64 | Mean : 86.993 | Mean : 45.8 | Mean : 0.5349 | Mean :12.96 | Mean :33.45 | Mean : 51.14 | Mean : 1.216 | Mean :0.01577 | Mean :-1 | Mean :30.86 | Mean : 40.98 | Mean : 74.1 | Mean :0.1001 | Mean :140.0 | Mean :0.2068 | Mean : 33.7 | Mean : 31.95 | Mean : 84.95 | Mean : 98.82 | |
| 3rd Qu.: 50.35 | 3rd Qu.:140.0 | 3rd Qu.:104.3 | 3rd Qu.: 15.72 | 3rd Qu.: 0.3400 | 3rd Qu.:0.7000 | 3rd Qu.:1172.5 | 3rd Qu.: 88.00 | 3rd Qu.:38.10 | 3rd Qu.:0.300 | 3rd Qu.: 12.60 | 3rd Qu.: 14.125 | 3rd Qu.:236.2 | 3rd Qu.: 9.100 | 3rd Qu.: 98.00 | 3rd Qu.: 35.125 | 3rd Qu.: 7.450 | 3rd Qu.:13.40 | 3rd Qu.:90.81 | 3rd Qu.:71.10 | 3rd Qu.: 0.0700 | 3rd Qu.: 96.00 | 3rd Qu.: 0.010 | 3rd Qu.: 92.50 | 3rd Qu.:40.4 | 3rd Qu.:11.480 | 3rd Qu.:11.68 | 3rd Qu.:351.0 | 3rd Qu.: 5.763 | 3rd Qu.: 5.000 | 3rd Qu.: 9.200 | 3rd Qu.: 1.300 | 3rd Qu.:7.000 | 3rd Qu.: 4.61 | 3rd Qu.:0.04000 | 3rd Qu.:2.420 | 3rd Qu.:4.620 | 3rd Qu.: 9.140 | 3rd Qu.:10.145 | 3rd Qu.: 6.800 | 3rd Qu.:11.50 | 3rd Qu.: 1439.8 | 3rd Qu.:43.59 | 3rd Qu.: 17.90 | 3rd Qu.:25.35 | 3rd Qu.:0.090 | 3rd Qu.:10.908 | 3rd Qu.:4.150 | 3rd Qu.: 43.25 | 3rd Qu.:323.4 | 3rd Qu.:25.02 | 3rd Qu.:2.190 | 3rd Qu.: 1065.0 | 3rd Qu.: 569.2 | 3rd Qu.:36.70 | 3rd Qu.: 62.788 | 3rd Qu.:102.6 | 3rd Qu.: 0.5600 | 3rd Qu.:14.30 | 3rd Qu.:36.50 | 3rd Qu.: 51.00 | 3rd Qu.: 1.242 | 3rd Qu.:0.02000 | 3rd Qu.:-1 | 3rd Qu.:32.02 | 3rd Qu.: 44.27 | 3rd Qu.:116.5 | 3rd Qu.:0.1100 | 3rd Qu.:142.3 | 3rd Qu.:0.2500 | 3rd Qu.: 46.0 | 3rd Qu.: 35.00 | 3rd Qu.:105.33 | 3rd Qu.: 96.00 | |
| Max. :50000.00 | Max. :178.0 | Max. :140.0 | Max. :104.80 | Max. :38.9200 | Max. :5.5500 | Max. :7500.0 | Max. :620.00 | Max. :48.60 | Max. :1.700 | Max. :1000.00 | Max. :505.700 | Max. :554.0 | Max. :35.200 | Max. :130.00 | Max. :443.000 | Max. :145.100 | Max. :24.60 | Max. :98.70 | Max. :80.30 | Max. :11.9500 | Max. :142.00 | Max. :250.000 | Max. :118.90 | Max. :52.3 | Max. :88.100 | Max. :70.40 | Max. :488.0 | Max. :10.590 | Max. :88.500 | Max. :59.000 | Max. :52.420 | Max. :7.565 | Max. :96.00 | Max. :0.38000 | Max. :2.790 | Max. :6.860 | Max. :29.650 | Max. :25.190 | Max. :360.600 | Max. :14.20 | Max. :50000.0 | Max. :86.90 | Max. :161.90 | Max. :60.00 | Max. :2.090 | Max. :60.000 | Max. :6.290 | Max. :783.00 | Max. :993.0 | Max. :33.10 | Max. :2.560 | Max. :70000.0 | Max. :1867.0 | Max. :58.60 | Max. :5000.000 | Max. :190.8 | Max. :33.9600 | Max. :24.20 | Max. :49.00 | Max. :732.00 | Max. :13.480 | Max. :0.09000 | Max. :-1 | Max. :50.80 | Max. :137.20 | Max. :320.0 | Max. :0.2700 | Max. :179.7 | Max. :0.5100 | Max. :106.0 | Max. :744.00 | Max. :224.00 | Max. :1363.00 | |
| NA’s :74 | NA’s :5 | NA’s :7 | NA’s :9 | NA’s :48 | NA’s :5 | NA’s :145 | NA’s :5 | NA’s :5 | NA’s :5 | NA’s :146 | NA’s :5 | NA’s :5 | NA’s :5 | NA’s :159 | NA’s :145 | NA’s :6 | NA’s :11 | NA’s :5 | NA’s :5 | NA’s :86 | NA’s :9 | NA’s :86 | NA’s :5 | NA’s :5 | NA’s :4 | NA’s :145 | NA’s :5 | NA’s :63 | NA’s :145 | NA’s :5 | NA’s :5 | NA’s :129 | NA’s :4 | NA’s :5 | NA’s :8 | NA’s :7 | NA’s :10 | NA’s :5 | NA’s :5 | NA’s :15 | NA’s :148 | NA’s :11 | NA’s :63 | NA’s :5 | NA’s :86 | NA’s :19 | NA’s :5 | NA’s :5 | NA’s :5 | NA’s :5 | NA’s :7 | NA’s :94 | NA’s :5 | NA’s :15 | NA’s :143 | NA’s :159 | NA’s :5 | NA’s :15 | NA’s :5 | NA’s :5 | NA’s :9 | NA’s :5 | NA’s :143 | NA’s :5 | NA’s :63 | NA’s :8 | NA’s :87 | NA’s :7 | NA’s :15 | NA’s :73 | NA’s :5 | NA’s :5 | NA’s :5 |
Wariancja
Ponieważ kolumna ‘Hypersensitive cardiac troponinI’ ma bardzo dużą wartość maksymalną, można sprawdzić wariancje każdej z kolumn.
data_process_var<- data_fill_summarise %>%
ungroup() %>%
select(-PATIENT_ID) %>%
summarise(across(everything(),
l.fns= ~var(.x, na.rm = T), ~var(.x, na.rm = T)))
data_process_median <- data_fill_summarise %>%
ungroup() %>%
select(-PATIENT_ID) %>%
summarise(across(everything(), .fns=~median(.x, na.rm = T)))
data_processed_var_median <- bind_rows(col=names(data_process_median),
var=unlist(data_process_var[1, ],
use.names=FALSE),
median=unlist(data_process_median[1, ],
use.names=FALSE))
kable(data_processed_var_median) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "500px")| col | var | median |
|---|---|---|
| Hypersensitive.cardiac.troponinI | 1.964032e+07 | 11.000 |
| hemoglobin | 3.774513e+02 | 127.000 |
| Serum.chloride | 4.209095e+01 | 101.450 |
| Prothrombin.time | 3.018172e+01 | 14.300 |
| procalcitonin | 9.601393e+00 | 0.100 |
| eosinophils… | 8.152250e-01 | 0.100 |
| Interleukin.2.receptor | 6.204319e+05 | 693.500 |
| Alkaline.phosphatase | 2.278591e+03 | 68.000 |
| albumin | 3.295290e+01 | 33.450 |
| basophil… | 3.990000e-02 | 0.200 |
| Interleukin.10 | 4.643130e+03 | 6.000 |
| Total.bilirubin | 1.022797e+03 | 9.900 |
| Platelet.count | 8.375741e+03 | 184.500 |
| monocytes… | 1.592388e+01 | 6.300 |
| antithrombin | 2.474903e+02 | 88.000 |
| Interleukin.8 | 4.337771e+03 | 16.450 |
| indirect.bilirubin | 7.958923e+01 | 5.200 |
| Red.blood.cell.distribution.width | 2.526058e+00 | 12.450 |
| neutrophils… | 2.692162e+02 | 77.900 |
| total.protein | 3.572654e+01 | 67.100 |
| Quantification.of.Treponema.pallidum.antibodies | 6.013917e-01 | 0.050 |
| Prothrombin.activity | 3.763158e+02 | 87.000 |
| HBsAg | 1.868855e+03 | 0.010 |
| mean.corpuscular.volume | 3.653049e+01 | 89.600 |
| hematocrit | 2.562566e+01 | 37.000 |
| White.blood.cell.count | 5.492334e+01 | 6.740 |
| Tumor.necrosis.factor.U.03B1. | 8.202053e+01 | 8.600 |
| mean.corpuscular.hemoglobin.concentration | 2.629360e+02 | 344.000 |
| fibrinogen | 2.848917e+00 | 4.580 |
| Interleukin.1ß | 4.652517e+01 | 5.000 |
| Urea | 4.999563e+01 | 5.275 |
| lymphocyte.count | 7.719553e+00 | 0.820 |
| PH.value | 4.687833e-01 | 6.500 |
| Red.blood.cell.count | 5.223115e+01 | 4.180 |
| Eosinophil.count | 2.575100e-03 | 0.010 |
| Corrected.calcium | 1.351020e-02 | 2.350 |
| Serum.potassium | 4.388906e-01 | 4.230 |
| glucose | 1.752148e+01 | 6.920 |
| neutrophils.count | 2.775600e+01 | 4.945 |
| Direct.bilirubin | 5.660994e+02 | 4.600 |
| Mean.platelet.volume | 9.880903e-01 | 10.800 |
| ferritin | 1.463976e+07 | 711.600 |
| RBC.distribution.width.SD | 3.114216e+01 | 40.100 |
| Thrombin.time | 7.772115e+01 | 16.600 |
| X…lymphocyte | 1.631137e+02 | 13.550 |
| HCV.antibody.quantification | 4.720360e-02 | 0.060 |
| D.D.dimer | 7.918990e+01 | 1.330 |
| Total.cholesterol | 7.676391e-01 | 3.505 |
| aspartate.aminotransferase | 3.506764e+03 | 29.000 |
| Uric.acid | 1.705143e+04 | 245.000 |
| HCO3. | 1.270170e+01 | 22.900 |
| calcium | 1.692260e-02 | 2.110 |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | 3.995339e+07 | 296.000 |
| Lactate.dehydrogenase | 1.010520e+05 | 325.500 |
| platelet.large.cell.ratio | 6.266403e+01 | 31.000 |
| Interleukin.6 | 1.373100e+05 | 22.125 |
| Fibrin.degradation.products | 3.813263e+03 | 5.100 |
| monocytes.count | 3.208191e+00 | 0.400 |
| PLT.distribution.width | 6.547088e+00 | 12.575 |
| globulin | 2.451536e+01 | 33.150 |
| X.U.03B3..glutamyl.transpeptidase | 5.301597e+03 | 31.000 |
| International.standard.ratio | 5.084200e-01 | 1.090 |
| basophil.count… | 1.972000e-04 | 0.010 |
| X2019.nCoV.nucleic.acid.detection | 0.000000e+00 | -1.000 |
| mean.corpuscular.hemoglobin | 7.770515e+00 | 30.800 |
| Activation.of.partial.thromboplastin.time | 7.604773e+01 | 39.450 |
| High.sensitivity.C.reactive.protein | 5.623949e+03 | 52.000 |
| HIV.antibody.quantification | 1.610300e-03 | 0.090 |
| serum.sodium | 3.792593e+01 | 139.800 |
| thrombocytocrit | 7.210400e-03 | 0.200 |
| ESR | 5.949799e+02 | 29.000 |
| glutamic.pyruvic.transaminase | 2.381624e+03 | 22.000 |
| eGFR | 8.810664e+02 | 90.100 |
| creatinine | 1.654261e+04 | 74.500 |
Można zauważyć, że kilka kolumn ma bardzo dużą wariancje w stosunku do mediany. W takim przypadku warto usunąć takie kolumn, jako, że mogą przeszkodzić w kolejnych analizach i w tworzeniu klasyfikatora. Zostanie to zrobione w sekcji poświęconej klasyfikatorowi, ponieważ warto najpierw najpierw pokazać je na wykresie. ### Wykresy Za pomocą poniższego wykresu interaktywnego, można wybrać do pokazania przebieg wartości danej kolumny lub wybrać kolumny dla wartości y oraz x, aby zobaczyć zależności.
n <- names(data_fill_summarise %>% select(!PATIENT_ID))
nn <- abbreviate(n)## Warning in abbreviate(n): 'abbreviate' użyte ze znakami nie będącymi ASCIIbuttons <- list()
buttons_x <- list()
id = 1
for (name in n ){
ly <- list(method = "update",
label = paste(nn[[name]], '(', id, ') as y', sep=''),
args = list(list(
y=list(data_fill_summarise[[name]])),
list(yaxis = list(title = name))
))
lx <- list(method = "update",
label = paste(nn[[name]], '(', id, ') as x', sep=''),
args = list(list(
x=list(data_fill_summarise[[name]])),
list(xaxis = list(title = name, domain = c(0.1, 1)))
))
buttons <- c(buttons, list(ly))
buttons_x <- c(buttons_x, list(lx))
id <- id + 1
}
lx <- list(method = "update",
label = 'None',
args = list(list(
x=list(seq(nrow(data_fill_summarise)))),
list(xaxis = list(title = 'row_id', domain = c(0.1, 1)))))
buttons_x <- c(list(lx), buttons_x)fig_many <- plot_ly(data_fill_summarise,
x = ~seq(nrow(data_fill_summarise)),
y = ~data_fill_summarise[[name[1]]], alpha = 0.3)
fig_many <- fig_many %>% add_markers(marker = list(line = list(color = "black", width = 0.5)))
fig_many <- fig_many %>% layout(
title = "Plot of one or the relationship between columns.",
xaxis = list(domain = c(0.1, 1), title = 'row_id'),
yaxis = list(title = name[1]),
updatemenus = list(
list(y = 0.8,
buttons = buttons
),
list(y = 0.6,
buttons = buttons_x
)
)
)
fig_manyKorelacja
Za pomocą korelacji można sprawdzić czy nie ma zależności pomiędzy kolumnami. Jeśli istnieją, to pewne kolumny będą zbędne i będzie można wyrzucić ze zbioru.
Uznano, że jeżeli współczynnik korelacji będzie większy bądź równy 0.8, to oznacza, że kolumny są skorelowane.
correl <- cor(data_fill_summarise[-1], use='complete.obs')## Warning in cor(data_fill_summarise[-1], use = "complete.obs"): odchylenie
## standardowe wynosi zerocorrel_table <- as_tibble(correl) %>% mutate(from=names(correl))
kable(correl_table) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "500px")| Hypersensitive.cardiac.troponinI | hemoglobin | Serum.chloride | Prothrombin.time | procalcitonin | eosinophils… | Interleukin.2.receptor | Alkaline.phosphatase | albumin | basophil… | Interleukin.10 | Total.bilirubin | Platelet.count | monocytes… | antithrombin | Interleukin.8 | indirect.bilirubin | Red.blood.cell.distribution.width | neutrophils… | total.protein | Quantification.of.Treponema.pallidum.antibodies | Prothrombin.activity | HBsAg | mean.corpuscular.volume | hematocrit | White.blood.cell.count | Tumor.necrosis.factor.U.03B1. | mean.corpuscular.hemoglobin.concentration | fibrinogen | Interleukin.1ß | Urea | lymphocyte.count | PH.value | Red.blood.cell.count | Eosinophil.count | Corrected.calcium | Serum.potassium | glucose | neutrophils.count | Direct.bilirubin | Mean.platelet.volume | ferritin | RBC.distribution.width.SD | Thrombin.time | X…lymphocyte | HCV.antibody.quantification | D.D.dimer | Total.cholesterol | aspartate.aminotransferase | Uric.acid | HCO3. | calcium | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Lactate.dehydrogenase | platelet.large.cell.ratio | Interleukin.6 | Fibrin.degradation.products | monocytes.count | PLT.distribution.width | globulin | X.U.03B3..glutamyl.transpeptidase | International.standard.ratio | basophil.count… | X2019.nCoV.nucleic.acid.detection | mean.corpuscular.hemoglobin | Activation.of.partial.thromboplastin.time | High.sensitivity.C.reactive.protein | HIV.antibody.quantification | serum.sodium | thrombocytocrit | ESR | glutamic.pyruvic.transaminase | eGFR | creatinine |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0000000 | 0.3090031 | -0.1514645 | 0.3980390 | 0.1842243 | -0.2579785 | 0.7003151 | 0.6879387 | -0.3550955 | -0.1151775 | 0.0800379 | 0.7305316 | -0.3910918 | -0.3775635 | -0.3288251 | 0.2179479 | 0.4189153 | 0.4528826 | 0.3758602 | -0.2290173 | 0.2139383 | -0.3401214 | 0.0476141 | 0.3900092 | 0.2630026 | 0.4800134 | 0.1850152 | 0.0710972 | -0.1776919 | -0.1007560 | 0.7522036 | -0.2673368 | -0.3087319 | -0.0191184 | -0.2505200 | 0.1400971 | -0.0343477 | -0.0959208 | 0.5034699 | 0.8398009 | 0.0749452 | 0.1044471 | 0.3933478 | 0.2070382 | -0.3530780 | -0.0320244 | 0.3017633 | 0.1452748 | 0.3336605 | 0.5465737 | -0.1284160 | -0.3552700 | 0.5325194 | 0.4411611 | 0.1356697 | 0.6089168 | 0.2909441 | 0.2816119 | 0.2995470 | 0.3047038 | 0.4708363 | 0.3926176 | 0.0965646 | NA | 0.2992647 | -0.1549458 | 0.6349779 | -0.0433099 | 0.1502153 | -0.4558767 | -0.0252301 | 0.2778420 | -0.4605798 | 0.2257688 |
| 0.3090031 | 1.0000000 | 0.1810569 | 0.0382213 | 0.1676988 | 0.0874823 | 0.2947947 | 0.1844285 | 0.0813186 | -0.1028427 | 0.0792011 | 0.3478646 | -0.2956977 | 0.0583325 | -0.1567856 | -0.0459101 | 0.3829235 | -0.2479188 | -0.1381375 | 0.2312710 | 0.2660614 | 0.0661188 | -0.2921729 | -0.0114770 | 0.9127091 | 0.3796424 | -0.0628034 | 0.3197929 | -0.1968912 | -0.1510211 | 0.1896620 | 0.2810759 | -0.3550320 | 0.6025731 | 0.1291319 | -0.0897263 | 0.2778291 | -0.0548264 | 0.1257392 | 0.3007155 | -0.0716891 | 0.0346474 | -0.3058716 | 0.1285247 | 0.1665054 | 0.0680778 | 0.0580267 | -0.2422309 | -0.0687146 | 0.3654918 | 0.1550968 | -0.0053645 | 0.1392709 | 0.1600551 | -0.0029766 | 0.0853344 | -0.0568803 | 0.3690220 | 0.1808126 | 0.0869484 | -0.1367618 | 0.0426136 | 0.1883283 | NA | 0.1800174 | 0.0364618 | -0.0092149 | -0.1545346 | 0.3859266 | -0.3815946 | -0.3484926 | -0.2000107 | 0.1210353 | -0.0063728 |
| -0.1514645 | 0.1810569 | 1.0000000 | 0.1375053 | 0.3830471 | -0.0273677 | 0.0545780 | 0.0952495 | -0.1077396 | 0.1523118 | 0.1879930 | -0.0748420 | -0.1956483 | -0.1280471 | -0.0091506 | 0.3145619 | 0.0408059 | -0.0593163 | 0.0251840 | -0.0322376 | 0.3902088 | -0.0644876 | -0.0383565 | -0.1784836 | 0.2239421 | 0.2838721 | 0.2051048 | -0.0356771 | -0.2217434 | 0.1819540 | 0.1741694 | 0.1966700 | -0.1011266 | 0.2211003 | -0.1010265 | -0.0021907 | 0.3546066 | -0.1587063 | 0.2322774 | -0.1681559 | 0.4955146 | 0.3534044 | -0.1654599 | 0.2212147 | 0.0157065 | 0.0866925 | 0.1447740 | -0.1069830 | 0.2082495 | -0.0075445 | -0.2814987 | -0.2048827 | 0.2536023 | 0.4657144 | 0.5044028 | -0.1228004 | -0.1047375 | 0.2413263 | 0.4472444 | 0.1996859 | -0.0255151 | 0.1503119 | 0.4609757 | NA | -0.1532263 | -0.1484664 | -0.1028728 | 0.0938636 | 0.8344035 | -0.1270841 | 0.0369669 | -0.0177794 | -0.0971718 | 0.0839090 |
| 0.3980390 | 0.0382213 | 0.1375053 | 1.0000000 | 0.5973899 | -0.2547790 | 0.6076711 | 0.3303331 | -0.5434632 | -0.0518024 | -0.0287514 | 0.4529765 | -0.4411533 | -0.5168092 | -0.4653382 | 0.2725951 | 0.3301520 | 0.1552547 | 0.5168957 | -0.6611779 | -0.1247091 | -0.9530525 | 0.1264155 | 0.1900203 | -0.0012916 | 0.4874862 | 0.2388218 | -0.0488167 | -0.4800636 | 0.1609289 | 0.6594697 | -0.4933711 | -0.1446578 | -0.1481752 | -0.1671540 | 0.5385248 | 0.3175438 | 0.3284598 | 0.6653062 | 0.4356759 | 0.2637485 | 0.4644739 | 0.0500288 | 0.3679043 | -0.4986410 | -0.0470497 | 0.6364930 | -0.0838046 | 0.5588319 | -0.0018173 | 0.0411577 | -0.4237210 | 0.7344790 | 0.6053722 | 0.2495990 | 0.4492479 | 0.5847069 | 0.2712789 | 0.4191249 | 0.1843016 | 0.2601249 | 0.9992779 | 0.4238636 | NA | 0.0576323 | -0.1655444 | 0.5003933 | -0.2943415 | 0.3728913 | -0.4562294 | -0.2116838 | 0.4211762 | -0.3135288 | 0.0668809 |
| 0.1842243 | 0.1676988 | 0.3830471 | 0.5973899 | 1.0000000 | -0.2212792 | 0.3321484 | 0.0806124 | -0.3725470 | 0.1385401 | -0.0147294 | 0.0796920 | -0.3664123 | -0.3765443 | -0.4333035 | 0.0293449 | 0.1325098 | 0.2686435 | 0.3442540 | -0.3416707 | -0.0292209 | -0.4491279 | -0.1173877 | 0.0183535 | 0.1435186 | 0.5718013 | -0.0299245 | 0.0429310 | -0.3668003 | -0.0742507 | 0.5848858 | -0.2991765 | -0.1355157 | 0.0174611 | -0.2159787 | 0.0252439 | 0.1938379 | 0.3579924 | 0.6416353 | 0.0127316 | 0.4042621 | 0.4181547 | 0.1671658 | 0.4001700 | -0.3229484 | 0.0747764 | 0.4088930 | -0.1964753 | 0.2647309 | 0.0879980 | -0.0061626 | -0.4552528 | 0.6300564 | 0.6129884 | 0.3814426 | 0.4652115 | 0.3686684 | 0.1629651 | 0.4388754 | 0.1953624 | 0.1509763 | 0.6093625 | 0.8084308 | NA | 0.0347825 | -0.0225270 | 0.4493481 | -0.2992783 | 0.4644850 | -0.3828781 | -0.1701500 | -0.0383567 | -0.2114159 | 0.2136953 |
| -0.2579785 | 0.0874823 | -0.0273677 | -0.2547790 | -0.2212792 | 1.0000000 | -0.4816381 | -0.0532359 | 0.4713006 | 0.6318460 | -0.0536297 | -0.1060302 | 0.3135019 | 0.4828294 | 0.1657326 | -0.3072045 | 0.1179633 | -0.1605387 | -0.7319357 | 0.4970316 | -0.1333605 | 0.1625446 | -0.2122609 | 0.1378107 | -0.0876245 | -0.2578909 | -0.3385928 | 0.4363618 | -0.1903502 | 0.1648465 | -0.4135664 | 0.7030217 | -0.1163047 | -0.0403701 | 0.8793301 | 0.1949485 | 0.0948393 | -0.3113629 | -0.4751558 | -0.2297294 | -0.1817210 | -0.2331957 | -0.0344347 | -0.2212843 | 0.7420387 | 0.3639310 | -0.1105000 | 0.3592424 | -0.3636272 | 0.1954313 | 0.3216523 | 0.6707114 | -0.4015080 | -0.3947446 | -0.2044245 | -0.4706342 | -0.2918482 | -0.1757736 | -0.2731519 | -0.1497582 | -0.1498797 | -0.2589159 | 0.0583089 | NA | 0.3555468 | -0.1059630 | -0.4926264 | 0.2673250 | 0.0191203 | 0.2898657 | -0.2042048 | -0.1799848 | 0.2338191 | -0.0538952 |
| 0.7003151 | 0.2947947 | 0.0545780 | 0.6076711 | 0.3321484 | -0.4816381 | 1.0000000 | 0.6266870 | -0.6125923 | -0.2070635 | 0.1601806 | 0.6601094 | -0.3445819 | -0.5525153 | -0.5171933 | 0.3815831 | 0.4229187 | 0.2977465 | 0.6030336 | -0.4590403 | 0.3240370 | -0.5322996 | 0.2934054 | 0.0652550 | 0.3121013 | 0.6070066 | 0.3305758 | -0.0694062 | 0.0654920 | -0.0776918 | 0.7576126 | -0.4846694 | -0.2834675 | 0.0936660 | -0.3607242 | 0.3045213 | 0.1865584 | 0.3171844 | 0.6483504 | 0.7190193 | 0.1803913 | 0.4329089 | 0.1012627 | 0.4692360 | -0.5909248 | -0.1423458 | 0.3885699 | -0.0236401 | 0.5070108 | 0.2721160 | -0.3947974 | -0.5788920 | 0.6767919 | 0.7041850 | 0.2118292 | 0.7052274 | 0.5266034 | 0.5268679 | 0.4078620 | 0.4685699 | 0.3445936 | 0.6052094 | 0.2691203 | NA | -0.0099663 | -0.1259129 | 0.7396206 | -0.1286339 | 0.1821875 | -0.3724890 | 0.1222387 | 0.2678890 | -0.4356152 | 0.1743956 |
| 0.6879387 | 0.1844285 | 0.0952495 | 0.3303331 | 0.0806124 | -0.0532359 | 0.6266870 | 1.0000000 | -0.4455452 | 0.1578840 | 0.1414066 | 0.6097361 | -0.3349623 | -0.3586896 | -0.2943820 | 0.4309871 | 0.4805493 | 0.4149290 | 0.2362062 | -0.2154628 | 0.5315726 | -0.3246787 | 0.1378474 | 0.3558477 | 0.1300490 | 0.3947607 | 0.3525166 | 0.0271688 | -0.1531382 | 0.0666721 | 0.5226442 | -0.1075708 | -0.3524802 | -0.0503717 | -0.0722155 | 0.3958766 | 0.1987950 | -0.0324993 | 0.3841223 | 0.6016592 | 0.0329782 | 0.4669722 | 0.4325527 | 0.3156283 | -0.2042296 | -0.0881063 | 0.4222412 | 0.4718722 | 0.4453646 | 0.3654745 | -0.3173311 | -0.3294636 | 0.4685761 | 0.5775344 | 0.0599776 | 0.3885533 | 0.4033742 | 0.1557469 | 0.1524749 | 0.5443837 | 0.6544855 | 0.3200495 | 0.1954710 | NA | 0.2371869 | -0.3907039 | 0.5155409 | 0.2159772 | 0.2421988 | -0.3735150 | 0.2244327 | 0.3937510 | -0.4729837 | 0.2599757 |
| -0.3550955 | 0.0813186 | -0.1077396 | -0.5434632 | -0.3725470 | 0.4713006 | -0.6125923 | -0.4455452 | 1.0000000 | 0.0829605 | -0.2877756 | -0.2882934 | 0.2898404 | 0.6724237 | 0.4172418 | -0.3736010 | -0.0825071 | -0.2724626 | -0.7949865 | 0.8015878 | -0.3410700 | 0.5123223 | -0.3510070 | -0.0026838 | 0.1462979 | -0.5883589 | -0.3412338 | 0.0086808 | 0.0032505 | 0.1223837 | -0.5756303 | 0.6327934 | -0.0206912 | 0.0267947 | 0.3070353 | -0.4492692 | -0.1802400 | -0.5220758 | -0.7276176 | -0.3658601 | -0.1526348 | -0.5423189 | -0.1073301 | -0.4932327 | 0.7942448 | -0.0674455 | -0.4870471 | -0.0503673 | -0.4665881 | 0.2227805 | 0.4022458 | 0.8633454 | -0.6878439 | -0.7400500 | -0.1455400 | -0.4588109 | -0.5208901 | -0.3043445 | -0.2291549 | -0.6817372 | -0.3366015 | -0.5467316 | -0.4348922 | NA | 0.0183993 | 0.3133523 | -0.5901055 | 0.0958651 | -0.1156707 | 0.2798369 | -0.3134659 | -0.3529552 | 0.3789646 | -0.0207951 |
| -0.1151775 | -0.1028427 | 0.1523118 | -0.0518024 | 0.1385401 | 0.6318460 | -0.2070635 | 0.1578840 | 0.0829605 | 1.0000000 | -0.0095624 | 0.0666399 | 0.0593519 | 0.3131026 | -0.0402343 | -0.1372842 | 0.2406486 | 0.2731926 | -0.4416733 | 0.2459617 | 0.0262860 | 0.0498325 | -0.1466810 | 0.1250564 | -0.1948492 | -0.0890583 | -0.1711620 | 0.2759533 | -0.2912064 | 0.0520150 | 0.0062345 | 0.3443989 | -0.2006560 | -0.0735208 | 0.4259060 | 0.2650904 | 0.0797730 | -0.1087953 | -0.1143103 | -0.0662241 | 0.0556113 | 0.0996401 | 0.2813457 | 0.0598588 | 0.4201767 | 0.2187524 | -0.0683962 | 0.3909316 | -0.1077815 | 0.2112073 | 0.0360748 | 0.2795003 | -0.0205297 | 0.0079828 | 0.0056721 | -0.1940021 | -0.1467801 | 0.0216271 | -0.0969316 | 0.2167861 | 0.1388448 | -0.0538476 | 0.5153256 | NA | 0.2591986 | -0.3858612 | -0.2451190 | 0.3350620 | 0.1282056 | 0.0533847 | -0.0021327 | -0.0115033 | -0.0164765 | 0.0621408 |
| 0.0800379 | 0.0792011 | 0.1879930 | -0.0287514 | -0.0147294 | -0.0536297 | 0.1601806 | 0.1414066 | -0.2877756 | -0.0095624 | 1.0000000 | 0.0486831 | -0.1630567 | -0.0666654 | -0.0027963 | 0.3988315 | -0.0381683 | -0.0370531 | 0.0218380 | -0.1121129 | 0.4436620 | 0.0099239 | 0.0331547 | -0.4289326 | 0.0582113 | 0.1951518 | 0.3681914 | 0.0011715 | 0.1611611 | 0.2775978 | 0.0192037 | 0.1257241 | -0.2135091 | 0.2245559 | -0.1296829 | 0.0130632 | -0.1158728 | 0.1499706 | 0.1646980 | 0.0765113 | 0.2327292 | 0.0735662 | -0.2506728 | 0.1213397 | 0.0102230 | -0.1462499 | -0.1030098 | 0.0573155 | 0.0863102 | -0.1184203 | -0.2455995 | -0.4020967 | -0.0234283 | 0.1809886 | 0.2772163 | 0.2239724 | 0.0680947 | 0.0896419 | 0.2220848 | 0.3211876 | -0.0709309 | -0.0245879 | 0.0736338 | NA | -0.3079177 | 0.0772345 | 0.0760772 | -0.0555324 | 0.0668798 | -0.1186420 | -0.0410406 | 0.0692483 | -0.1112860 | -0.0511809 |
| 0.7305316 | 0.3478646 | -0.0748420 | 0.4529765 | 0.0796920 | -0.1060302 | 0.6601094 | 0.6097361 | -0.2882934 | 0.0666399 | 0.0486831 | 1.0000000 | -0.3114480 | -0.1928123 | -0.2513338 | 0.2547137 | 0.8639484 | 0.3256451 | 0.2026632 | -0.0912695 | 0.2424613 | -0.4220731 | 0.1663149 | 0.1324094 | 0.3349626 | 0.4074319 | 0.1289871 | -0.0194965 | -0.1641342 | 0.1508499 | 0.5872545 | -0.1642332 | -0.4291879 | 0.1303889 | -0.1180110 | 0.3897107 | 0.0329693 | -0.0926059 | 0.4283628 | 0.9373167 | -0.0315523 | 0.1220141 | 0.1242312 | 0.0559811 | -0.2019965 | -0.0946356 | 0.1304505 | 0.0317821 | 0.2386778 | 0.3363288 | -0.1600498 | -0.1446451 | 0.3827430 | 0.3487747 | 0.0355122 | 0.3734712 | 0.0917993 | 0.5566360 | 0.2471154 | 0.3568623 | 0.2538221 | 0.4403364 | 0.1659461 | NA | 0.0770348 | -0.2576386 | 0.3485024 | 0.1098666 | 0.2193127 | -0.4070775 | 0.0376302 | 0.1114903 | -0.2159744 | -0.0521219 |
| -0.3910918 | -0.2956977 | -0.1956483 | -0.4411533 | -0.3664123 | 0.3135019 | -0.3445819 | -0.3349623 | 0.2898404 | 0.0593519 | -0.1630567 | -0.3114480 | 1.0000000 | 0.0389492 | 0.2726410 | -0.2884755 | -0.1872506 | -0.2174102 | -0.2351691 | 0.3551400 | -0.1300465 | 0.3296528 | 0.3204695 | -0.2933674 | -0.2841255 | -0.2274486 | -0.2605076 | -0.0739318 | 0.5066729 | -0.1143723 | -0.5412885 | 0.3642304 | 0.1444717 | 0.0586711 | 0.3465992 | 0.0328022 | 0.0378419 | -0.1617256 | -0.4283797 | -0.3455713 | -0.4415720 | -0.5270410 | -0.2205896 | -0.1030682 | 0.2407797 | 0.1013428 | -0.3735566 | 0.2075380 | -0.5860381 | -0.1056313 | 0.0296906 | 0.4105682 | -0.5605206 | -0.5152761 | -0.4636460 | -0.2223204 | -0.4128226 | -0.2460695 | -0.5337813 | -0.0834071 | -0.3641106 | -0.4514829 | -0.1821533 | NA | -0.1997099 | 0.1675716 | -0.2937644 | 0.3502449 | -0.3497214 | 0.9577341 | 0.3447483 | -0.4910935 | 0.2801776 | -0.2112968 |
| -0.3775635 | 0.0583325 | -0.1280471 | -0.5168092 | -0.3765443 | 0.4828294 | -0.5525153 | -0.3586896 | 0.6724237 | 0.3131026 | -0.0666654 | -0.1928123 | 0.0389492 | 1.0000000 | 0.1598341 | -0.3112433 | -0.0133799 | -0.2241494 | -0.8124883 | 0.6514809 | -0.1935897 | 0.4732803 | -0.1896121 | 0.0434262 | 0.0260621 | -0.6162041 | -0.1998634 | 0.2178063 | 0.0427266 | 0.0167379 | -0.4899764 | 0.5022971 | 0.1154986 | -0.1037955 | 0.2194846 | -0.2001576 | -0.2755028 | -0.2968019 | -0.7163866 | -0.2579619 | -0.0366773 | -0.3201999 | -0.0715942 | -0.2963703 | 0.7326184 | 0.0491496 | -0.5941878 | -0.0953373 | -0.4103231 | 0.0594236 | 0.1879401 | 0.6709072 | -0.5935588 | -0.6485240 | -0.0562270 | -0.4257810 | -0.5050610 | 0.0356312 | -0.2088553 | -0.3417552 | -0.1988647 | -0.5163053 | -0.2887744 | NA | 0.1601441 | 0.1104936 | -0.5800599 | 0.1260111 | -0.1757411 | 0.0597689 | -0.2098273 | -0.1787774 | 0.2819261 | -0.0843475 |
| -0.3288251 | -0.1567856 | -0.0091506 | -0.4653382 | -0.4333035 | 0.1657326 | -0.5171933 | -0.2943820 | 0.4172418 | -0.0402343 | -0.0027963 | -0.2513338 | 0.2726410 | 0.1598341 | 1.0000000 | -0.1910411 | -0.0968698 | -0.4672106 | -0.2935090 | 0.3733854 | 0.0827931 | 0.3666999 | -0.3212668 | -0.1414148 | -0.1518509 | -0.3465318 | -0.3041563 | -0.1190128 | 0.1776776 | 0.0497925 | -0.5520476 | 0.3403386 | 0.0078226 | 0.0838571 | 0.2300811 | -0.2110288 | -0.1942712 | -0.4185976 | -0.3767206 | -0.3064042 | -0.2178867 | -0.3195130 | -0.3150168 | -0.5787747 | 0.3257298 | 0.0070820 | -0.3378127 | 0.2084531 | -0.4150858 | -0.2788321 | 0.2827438 | 0.3144671 | -0.4883014 | -0.3868160 | -0.1614265 | -0.5544185 | -0.4398382 | -0.2677984 | -0.2097430 | -0.3125551 | -0.3300379 | -0.4790366 | -0.2741626 | NA | -0.1366338 | 0.0444249 | -0.5138709 | 0.1093798 | -0.0239250 | 0.3390471 | 0.1777997 | -0.3356507 | 0.3475057 | -0.2880467 |
| 0.2179479 | -0.0459101 | 0.3145619 | 0.2725951 | 0.0293449 | -0.3072045 | 0.3815831 | 0.4309871 | -0.3736010 | -0.1372842 | 0.3988315 | 0.2547137 | -0.2884755 | -0.3112433 | -0.1910411 | 1.0000000 | 0.1418244 | 0.0933722 | 0.3612281 | -0.3359272 | 0.4109918 | -0.2597190 | 0.2810189 | -0.0032080 | -0.0187994 | 0.1743802 | 0.8374025 | 0.0039631 | 0.0131987 | 0.6707299 | 0.3032270 | -0.2764663 | -0.1844999 | 0.0305541 | -0.3049202 | 0.2478162 | 0.0891452 | 0.2052858 | 0.4638965 | 0.2229799 | 0.0763359 | 0.2055034 | 0.0020757 | 0.2956022 | -0.3495871 | -0.0336769 | 0.3375962 | 0.0424680 | 0.2949266 | -0.1432439 | -0.4727844 | -0.3660578 | 0.1300495 | 0.3744051 | 0.0856362 | 0.3871774 | 0.4166113 | 0.1382459 | 0.0905792 | 0.4100502 | 0.2150135 | 0.2774001 | 0.0658406 | NA | -0.0564609 | -0.2267015 | 0.3430234 | 0.0063870 | 0.2877064 | -0.3276985 | 0.3039393 | 0.2730155 | -0.2888526 | 0.0361304 |
| 0.4189153 | 0.3829235 | 0.0408059 | 0.3301520 | 0.1325098 | 0.1179633 | 0.4229187 | 0.4805493 | -0.0825071 | 0.2406486 | -0.0381683 | 0.8639484 | -0.1872506 | -0.0133799 | -0.0968698 | 0.1418244 | 1.0000000 | 0.1393839 | -0.0659140 | 0.1611638 | 0.2855818 | -0.3271790 | 0.0686218 | -0.0279419 | 0.3768915 | 0.3086121 | 0.0423151 | -0.0211648 | -0.1894901 | 0.2316612 | 0.3446860 | 0.1152698 | -0.4284796 | 0.2296280 | 0.0954322 | 0.3766221 | 0.0957682 | -0.0251399 | 0.2466264 | 0.6411761 | -0.1674202 | 0.0890478 | -0.0190532 | 0.0487445 | 0.0711631 | -0.1048014 | 0.0572960 | 0.0945040 | 0.0013797 | 0.2041206 | -0.1122097 | 0.0449012 | 0.2247050 | 0.2511617 | -0.1124543 | 0.1781163 | -0.0016262 | 0.4486630 | 0.0561350 | 0.3275437 | 0.0670752 | 0.3146475 | 0.3045819 | NA | -0.0254117 | -0.2250578 | 0.1280014 | 0.1803651 | 0.2582761 | -0.3000268 | 0.0223691 | -0.1671202 | -0.0487550 | -0.1005604 |
| 0.4528826 | -0.2479188 | -0.0593163 | 0.1552547 | 0.2686435 | -0.1605387 | 0.2977465 | 0.4149290 | -0.2724626 | 0.2731926 | -0.0370531 | 0.3256451 | -0.2174102 | -0.2241494 | -0.4672106 | 0.0933722 | 0.1393839 | 1.0000000 | 0.1958001 | -0.2273204 | 0.0792072 | -0.0771240 | 0.2756732 | 0.1903288 | -0.1542777 | 0.2057732 | 0.0208278 | -0.1418596 | -0.1193026 | -0.1021859 | 0.4873799 | -0.3079636 | -0.1862007 | -0.3276759 | -0.3116205 | -0.1471417 | -0.1706600 | -0.0201533 | 0.2603553 | 0.3911630 | 0.1960919 | 0.2535280 | 0.8246578 | 0.2018123 | -0.1984171 | -0.0826300 | 0.1126348 | 0.1813207 | 0.3977596 | 0.4103786 | -0.2954252 | -0.2628289 | 0.3224972 | 0.2912273 | 0.1681366 | 0.3834756 | 0.0921268 | -0.0656917 | 0.1831879 | 0.1744106 | 0.4523422 | 0.1590467 | 0.1485570 | NA | 0.0604424 | -0.1496611 | 0.4097318 | 0.2675817 | -0.0651121 | -0.2444651 | 0.0782634 | 0.1800617 | -0.3754741 | 0.3111955 |
| 0.3758602 | -0.1381375 | 0.0251840 | 0.5168957 | 0.3442540 | -0.7319357 | 0.6030336 | 0.2362062 | -0.7949865 | -0.4416733 | 0.0218380 | 0.2026632 | -0.2351691 | -0.8124883 | -0.2935090 | 0.3612281 | -0.0659140 | 0.1958001 | 1.0000000 | -0.7876116 | 0.0995864 | -0.4324676 | 0.2577425 | 0.0525291 | -0.1189300 | 0.5347745 | 0.3119202 | -0.1377427 | 0.0487689 | -0.0656945 | 0.6084646 | -0.8173481 | 0.0916520 | -0.0497649 | -0.4678084 | 0.1654104 | 0.0356509 | 0.4650862 | 0.7880123 | 0.3335478 | 0.1252229 | 0.3925554 | 0.0793307 | 0.3158118 | -0.9900159 | -0.0016183 | 0.5046739 | -0.1150459 | 0.4562404 | -0.2059236 | -0.3241863 | -0.7864103 | 0.6568487 | 0.6514192 | 0.1310120 | 0.4829773 | 0.5669557 | 0.2575160 | 0.2428026 | 0.3655689 | 0.2750728 | 0.5252350 | 0.2259505 | NA | -0.0620973 | -0.1843326 | 0.6491214 | -0.2687250 | 0.0753131 | -0.2477217 | 0.2642131 | 0.3301822 | -0.3527568 | 0.0512685 |
| -0.2290173 | 0.2312710 | -0.0322376 | -0.6611779 | -0.3416707 | 0.4970316 | -0.4590403 | -0.2154628 | 0.8015878 | 0.2459617 | -0.1121129 | -0.0912695 | 0.3551400 | 0.6514809 | 0.3733854 | -0.3359272 | 0.1611638 | -0.2273204 | -0.7876116 | 1.0000000 | 0.0056481 | 0.6240263 | -0.3309271 | -0.1226921 | 0.2685267 | -0.3744926 | -0.2680909 | 0.0544507 | 0.2155854 | -0.0042735 | -0.5193216 | 0.7504424 | -0.1207329 | 0.2226552 | 0.2965061 | -0.3399369 | -0.1164153 | -0.4794566 | -0.6474278 | -0.2210211 | -0.1860701 | -0.5117192 | -0.1440696 | -0.3276132 | 0.7755694 | 0.0290645 | -0.5719020 | 0.0901896 | -0.5466055 | 0.2874081 | 0.1056014 | 0.7184968 | -0.6305675 | -0.5606912 | -0.1490489 | -0.3071201 | -0.5805838 | -0.0880986 | -0.2276850 | -0.1518816 | -0.3187638 | -0.6707541 | -0.2188365 | NA | -0.0107608 | 0.2635648 | -0.4759947 | 0.2587999 | -0.0851461 | 0.3327046 | -0.0425600 | -0.5278164 | 0.3562557 | -0.0530645 |
| 0.2139383 | 0.2660614 | 0.3902088 | -0.1247091 | -0.0292209 | -0.1333605 | 0.3240370 | 0.5315726 | -0.3410700 | 0.0262860 | 0.4436620 | 0.2424613 | -0.1300465 | -0.1935897 | 0.0827931 | 0.4109918 | 0.2855818 | 0.0792072 | 0.0995864 | 0.0056481 | 1.0000000 | 0.1148140 | 0.1886740 | -0.2483061 | 0.2418541 | 0.2764819 | 0.3594359 | 0.0705654 | 0.2706100 | -0.0586191 | 0.0591751 | 0.1513992 | -0.0960441 | 0.3011864 | -0.1368103 | -0.0016023 | 0.0845338 | 0.1210991 | 0.1202582 | 0.1792057 | -0.0847555 | 0.2750373 | -0.0706407 | 0.3448085 | -0.0690701 | 0.0322620 | -0.0904409 | 0.2470792 | 0.0596690 | -0.0085455 | -0.4100273 | -0.4209381 | 0.0862356 | 0.4205461 | -0.0237314 | 0.1610899 | -0.0840316 | 0.0928023 | 0.0432006 | 0.5639013 | -0.0521085 | -0.1283056 | 0.1974954 | NA | -0.1214939 | -0.1326115 | 0.0714990 | 0.4896005 | 0.2993491 | -0.1687182 | 0.4313171 | -0.1515241 | -0.2283080 | 0.0760564 |
| -0.3401214 | 0.0661188 | -0.0644876 | -0.9530525 | -0.4491279 | 0.1625446 | -0.5322996 | -0.3246787 | 0.5123223 | 0.0498325 | 0.0099239 | -0.4220731 | 0.3296528 | 0.4732803 | 0.3666999 | -0.2597190 | -0.3271790 | -0.0771240 | -0.4324676 | 0.6240263 | 0.1148140 | 1.0000000 | -0.1598955 | -0.1909186 | 0.1308230 | -0.3747132 | -0.2304200 | 0.0708478 | 0.3939577 | -0.1944154 | -0.5327571 | 0.4321865 | 0.0824825 | 0.2038148 | 0.0663270 | -0.6356143 | -0.3317551 | -0.3111788 | -0.5382990 | -0.3903563 | -0.2233152 | -0.3751983 | -0.0214997 | -0.3354372 | 0.4119016 | 0.0759500 | -0.5978872 | -0.0270687 | -0.4660248 | 0.0942628 | -0.0208003 | 0.3456279 | -0.6338506 | -0.5225317 | -0.2053035 | -0.3864678 | -0.5528326 | -0.1575724 | -0.3340775 | -0.1778663 | -0.2279243 | -0.9463288 | -0.3213795 | NA | -0.0453409 | 0.1859026 | -0.4445034 | 0.2370960 | -0.2463999 | 0.3353058 | 0.1330878 | -0.3967137 | 0.2735754 | -0.0063052 |
| 0.0476141 | -0.2921729 | -0.0383565 | 0.1264155 | -0.1173877 | -0.2122609 | 0.2934054 | 0.1378474 | -0.3510070 | -0.1466810 | 0.0331547 | 0.1663149 | 0.3204695 | -0.1896121 | -0.3212668 | 0.2810189 | 0.0686218 | 0.2756732 | 0.2577425 | -0.3309271 | 0.1886740 | -0.1598955 | 1.0000000 | -0.2031558 | -0.2198340 | -0.0499045 | 0.3003600 | -0.2478189 | 0.3751225 | 0.0379557 | 0.0379915 | -0.2353957 | 0.2903189 | -0.1453653 | -0.1958940 | 0.1609997 | -0.0206741 | 0.2007336 | 0.0390161 | 0.1888297 | -0.2328947 | -0.0309244 | 0.0641757 | 0.2862006 | -0.2826244 | -0.0581922 | -0.1014671 | 0.0388726 | 0.0159557 | -0.2522827 | -0.3718132 | -0.2732411 | -0.0289622 | -0.0150426 | -0.2520481 | 0.2275847 | -0.0296593 | 0.1595086 | -0.1994929 | 0.2392858 | -0.0574080 | 0.1258652 | -0.0806868 | NA | -0.2852008 | 0.1349882 | 0.2076236 | 0.4478112 | -0.1457239 | 0.2726511 | 0.4701264 | -0.0302492 | 0.0206588 | -0.2138649 |
| 0.3900092 | -0.0114770 | -0.1784836 | 0.1900203 | 0.0183535 | 0.1378107 | 0.0652550 | 0.3558477 | -0.0026838 | 0.1250564 | -0.4289326 | 0.1324094 | -0.2933674 | 0.0434262 | -0.1414148 | -0.0032080 | -0.0279419 | 0.1903288 | 0.0525291 | -0.1226921 | -0.2483061 | -0.1909186 | -0.2031558 | 1.0000000 | -0.1589695 | -0.0497312 | -0.0397781 | 0.3019610 | -0.3815091 | -0.0723383 | 0.2747184 | -0.1582897 | -0.0315354 | -0.4688105 | 0.1294020 | 0.1606493 | 0.0883187 | -0.2208636 | 0.0809412 | 0.2382741 | 0.1736064 | 0.2229608 | 0.6157970 | -0.0137419 | -0.0716961 | 0.2562957 | 0.3937564 | 0.2083639 | 0.2419268 | 0.3918809 | 0.2929515 | 0.1593078 | 0.1910259 | 0.0476063 | 0.1324115 | 0.0178214 | 0.2935304 | -0.0096758 | 0.1237047 | -0.1022451 | 0.5651296 | 0.1925131 | -0.0505621 | NA | 0.8199013 | -0.4685250 | 0.2294265 | -0.1732665 | 0.1078506 | -0.2697048 | 0.0002488 | 0.5269593 | -0.2916853 | 0.3591592 |
| 0.2630026 | 0.9127091 | 0.2239421 | -0.0012916 | 0.1435186 | -0.0876245 | 0.3121013 | 0.1300490 | 0.1462979 | -0.1948492 | 0.0582113 | 0.3349626 | -0.2841255 | 0.0260621 | -0.1518509 | -0.0187994 | 0.3768915 | -0.1542777 | -0.1189300 | 0.2685267 | 0.2418541 | 0.1308230 | -0.2198340 | -0.1589695 | 1.0000000 | 0.3650680 | 0.0115504 | -0.0362710 | -0.1810925 | -0.1274147 | 0.1617597 | 0.2820108 | -0.3628281 | 0.7197390 | -0.0703069 | -0.1899341 | 0.3392892 | -0.0874288 | 0.0960324 | 0.2801136 | -0.0442060 | 0.0098730 | -0.2822397 | 0.1326255 | 0.1531684 | -0.2326516 | 0.0307099 | -0.3084214 | -0.0108591 | 0.3972309 | 0.0594024 | -0.0071639 | 0.0999760 | 0.1235547 | 0.0342844 | 0.0948435 | -0.1228993 | 0.3489414 | 0.2202618 | 0.0364759 | -0.1863237 | 0.0024631 | 0.0845592 | NA | -0.1285389 | 0.1310063 | -0.0301929 | -0.1625246 | 0.3503402 | -0.3494505 | -0.3604930 | -0.1984400 | 0.1587826 | -0.0172558 |
| 0.4800134 | 0.3796424 | 0.2838721 | 0.4874862 | 0.5718013 | -0.2578909 | 0.6070066 | 0.3947607 | -0.5883589 | -0.0890583 | 0.1951518 | 0.4074319 | -0.2274486 | -0.6162041 | -0.3465318 | 0.1743802 | 0.3086121 | 0.2057732 | 0.5347745 | -0.3744926 | 0.2764819 | -0.3747132 | -0.0499045 | -0.0497312 | 0.3650680 | 1.0000000 | 0.0851963 | -0.0140570 | -0.2398841 | -0.1518876 | 0.6275542 | -0.2202643 | -0.3403618 | 0.2738403 | -0.0840891 | 0.2488668 | 0.4359226 | 0.2785948 | 0.7785524 | 0.4056423 | 0.2063537 | 0.3294871 | 0.0063242 | 0.4489461 | -0.4874915 | 0.0027889 | 0.5601885 | 0.0590597 | 0.2159009 | 0.2464470 | -0.2571017 | -0.5281817 | 0.5837188 | 0.6912965 | 0.2516651 | 0.3915041 | 0.3278679 | 0.3512996 | 0.3920549 | 0.4635453 | 0.1451303 | 0.4943155 | 0.5461311 | NA | -0.0349296 | -0.3161023 | 0.4318080 | -0.2719080 | 0.4334738 | -0.2658673 | -0.1364015 | -0.0042751 | -0.3140646 | 0.0757404 |
| 0.1850152 | -0.0628034 | 0.2051048 | 0.2388218 | -0.0299245 | -0.3385928 | 0.3305758 | 0.3525166 | -0.3412338 | -0.1711620 | 0.3681914 | 0.1289871 | -0.2605076 | -0.1998634 | -0.3041563 | 0.8374025 | 0.0423151 | 0.0208278 | 0.3119202 | -0.2680909 | 0.3594359 | -0.2304200 | 0.3003600 | -0.0397781 | 0.0115504 | 0.0851963 | 1.0000000 | -0.0873167 | -0.0209641 | 0.4910257 | 0.2365978 | -0.1772083 | 0.0427701 | 0.0299769 | -0.3653766 | 0.2354991 | 0.1255773 | 0.3350186 | 0.2767831 | 0.1154399 | -0.0523073 | 0.1231979 | -0.0335690 | 0.5608042 | -0.3212710 | -0.1823801 | 0.3248794 | 0.0456819 | 0.2872742 | -0.0996743 | -0.4870366 | -0.3804133 | 0.1685687 | 0.2902280 | -0.0553076 | 0.4626031 | 0.4204615 | 0.0576254 | -0.0906596 | 0.4508235 | 0.1599666 | 0.2436113 | -0.0351940 | NA | -0.1448905 | -0.0228839 | 0.3416510 | -0.0165658 | 0.1500682 | -0.3083563 | 0.1537550 | 0.3473751 | -0.3475484 | 0.1513592 |
| 0.0710972 | 0.3197929 | -0.0356771 | -0.0488167 | 0.0429310 | 0.4363618 | -0.0694062 | 0.0271688 | 0.0086808 | 0.2759533 | 0.0011715 | -0.0194965 | -0.0739318 | 0.2178063 | -0.1190128 | 0.0039631 | -0.0211648 | -0.1418596 | -0.1377427 | 0.0544507 | 0.0705654 | 0.0708478 | -0.2478189 | 0.3019610 | -0.0362710 | -0.0140570 | -0.0873167 | 1.0000000 | -0.0487331 | -0.0136273 | 0.0726018 | 0.0892976 | -0.1160975 | -0.1814475 | 0.4238557 | 0.0500750 | -0.2062661 | 0.0656344 | -0.0091151 | -0.0049249 | -0.1199267 | 0.0513219 | -0.0337282 | 0.0603137 | 0.1081047 | 0.8211721 | -0.0130207 | 0.0017223 | -0.0986635 | 0.1423610 | 0.1873388 | 0.1218197 | 0.0186128 | 0.0269827 | -0.1567471 | 0.0095255 | 0.0920492 | 0.0805062 | -0.1434127 | 0.0624946 | 0.0339178 | -0.0403464 | 0.1973807 | NA | 0.7850273 | -0.1757300 | -0.0132911 | 0.0491646 | 0.1218255 | -0.1423511 | -0.0710535 | -0.0621849 | -0.1924271 | 0.2045414 |
| -0.1776919 | -0.1968912 | -0.2217434 | -0.4800636 | -0.3668003 | -0.1903502 | 0.0654920 | -0.1531382 | 0.0032505 | -0.2912064 | 0.1611611 | -0.1641342 | 0.5066729 | 0.0427266 | 0.1776776 | 0.0131987 | -0.1894901 | -0.1193026 | 0.0487689 | 0.2155854 | 0.2706100 | 0.3939577 | 0.3751225 | -0.3815091 | -0.1810925 | -0.2398841 | -0.0209641 | -0.0487331 | 1.0000000 | -0.1616126 | -0.3164195 | -0.0895302 | 0.2111657 | 0.0470294 | -0.1795468 | -0.2242048 | -0.3308251 | 0.0935163 | -0.2720502 | -0.1060237 | -0.1478548 | -0.2049542 | -0.2125934 | -0.0857176 | -0.0793357 | 0.0628184 | -0.5319130 | -0.0757172 | -0.2559915 | -0.2612465 | -0.4043290 | -0.0494816 | -0.3562606 | -0.1809822 | -0.1385873 | 0.1369924 | -0.1998038 | 0.0181695 | -0.1745886 | 0.1650045 | -0.2390063 | -0.4872883 | -0.2968178 | NA | -0.2425506 | 0.3390412 | 0.0916729 | 0.2455184 | -0.4950337 | 0.5237464 | 0.6408035 | -0.4046041 | 0.1490965 | -0.1504464 |
| -0.1007560 | -0.1510211 | 0.1819540 | 0.1609289 | -0.0742507 | 0.1648465 | -0.0776918 | 0.0666721 | 0.1223837 | 0.0520150 | 0.2775978 | 0.1508499 | -0.1143723 | 0.0167379 | 0.0497925 | 0.6707299 | 0.2316612 | -0.1021859 | -0.0656945 | -0.0042735 | -0.0586191 | -0.1944154 | 0.0379557 | -0.0723383 | -0.1274147 | -0.1518876 | 0.4910257 | -0.0136273 | -0.1616126 | 1.0000000 | -0.0183581 | 0.0326596 | -0.1802453 | -0.0051320 | 0.1085575 | 0.2217156 | -0.0506113 | -0.0250539 | 0.1496683 | 0.0041606 | 0.0346997 | -0.0496045 | -0.1240335 | -0.1662495 | 0.0856324 | -0.0544381 | 0.2069887 | 0.0092503 | 0.0810734 | -0.2289943 | -0.1359580 | 0.1597178 | -0.1458259 | -0.0168519 | 0.0313521 | 0.0276635 | 0.2414599 | -0.0138517 | -0.0341942 | -0.0266873 | 0.0637338 | 0.1630721 | -0.0319428 | NA | -0.1075011 | -0.0574901 | 0.0114366 | -0.0672509 | 0.1752678 | -0.1463879 | -0.0121996 | 0.1481537 | 0.0722144 | -0.1430295 |
| 0.7522036 | 0.1896620 | 0.1741694 | 0.6594697 | 0.5848858 | -0.4135664 | 0.7576126 | 0.5226442 | -0.5756303 | 0.0062345 | 0.0192037 | 0.5872545 | -0.5412885 | -0.4899764 | -0.5520476 | 0.3032270 | 0.3446860 | 0.4873799 | 0.6084646 | -0.5193216 | 0.0591751 | -0.5327571 | 0.0379915 | 0.2747184 | 0.1617597 | 0.6275542 | 0.2365978 | 0.0726018 | -0.3164195 | -0.0183581 | 1.0000000 | -0.5481559 | -0.3096638 | -0.0962845 | -0.3765073 | 0.2157655 | 0.0154320 | 0.2004957 | 0.8001254 | 0.6502469 | 0.3652139 | 0.3993858 | 0.3479853 | 0.3865486 | -0.6019803 | -0.0117338 | 0.4977046 | -0.0899425 | 0.5016640 | 0.3580457 | -0.3274796 | -0.5723226 | 0.8428192 | 0.7461404 | 0.3712445 | 0.6325513 | 0.5128824 | 0.4609837 | 0.4677771 | 0.3493955 | 0.5334115 | 0.6665499 | 0.4805674 | NA | 0.2109969 | -0.2960997 | 0.7213911 | -0.3034006 | 0.3382779 | -0.5786015 | -0.0943950 | 0.3289082 | -0.5718451 | 0.3083112 |
| -0.2673368 | 0.2810759 | 0.1966700 | -0.4933711 | -0.2991765 | 0.7030217 | -0.4846694 | -0.1075708 | 0.6327934 | 0.3443989 | 0.1257241 | -0.1642332 | 0.3642304 | 0.5022971 | 0.3403386 | -0.2764663 | 0.1152698 | -0.3079636 | -0.8173481 | 0.7504424 | 0.1513992 | 0.4321865 | -0.2353957 | -0.1582897 | 0.2820108 | -0.2202643 | -0.1772083 | 0.0892976 | -0.0895302 | 0.0326596 | -0.5481559 | 1.0000000 | -0.1947563 | 0.2883311 | 0.5450969 | -0.0956674 | 0.1595787 | -0.4410488 | -0.6107087 | -0.3073988 | -0.2133373 | -0.3947869 | -0.2442398 | -0.1415782 | 0.8546539 | 0.0165651 | -0.3562744 | 0.2262591 | -0.4888212 | 0.3084766 | 0.2652950 | 0.6017271 | -0.5591793 | -0.4818549 | -0.1813364 | -0.4508752 | -0.5565374 | -0.1876936 | -0.2425653 | -0.1410853 | -0.3767710 | -0.5003404 | -0.1029378 | NA | -0.0436212 | 0.1182985 | -0.6177578 | 0.2950796 | 0.1618602 | 0.3553501 | -0.2255494 | -0.3745040 | 0.2023409 | 0.0456162 |
| -0.3087319 | -0.3550320 | -0.1011266 | -0.1446578 | -0.1355157 | -0.1163047 | -0.2834675 | -0.3524802 | -0.0206912 | -0.2006560 | -0.2135091 | -0.4291879 | 0.1444717 | 0.1154986 | 0.0078226 | -0.1844999 | -0.4284796 | -0.1862007 | 0.0916520 | -0.1207329 | -0.0960441 | 0.0824825 | 0.2903189 | -0.0315354 | -0.3628281 | -0.3403618 | 0.0427701 | -0.1160975 | 0.2111657 | -0.1802453 | -0.3096638 | -0.1947563 | 1.0000000 | -0.1431770 | -0.0421370 | -0.0996884 | -0.0128635 | 0.2309243 | -0.2431083 | -0.3504500 | -0.0690165 | -0.1801972 | -0.0971172 | 0.0681386 | -0.1353150 | 0.0658866 | -0.1103329 | -0.0843730 | -0.1441134 | -0.5466721 | 0.0035245 | -0.0581169 | -0.1001514 | -0.1655737 | -0.0954496 | -0.1453015 | -0.0816062 | -0.1856198 | -0.2013727 | -0.0993249 | -0.2346339 | -0.1366889 | -0.1432002 | NA | -0.0828695 | 0.3018740 | -0.1112623 | 0.1782008 | -0.2169466 | 0.1552143 | 0.2187536 | 0.0331291 | 0.3414679 | -0.3144391 |
| -0.0191184 | 0.6025731 | 0.2211003 | -0.1481752 | 0.0174611 | -0.0403701 | 0.0936660 | -0.0503717 | 0.0267947 | -0.0735208 | 0.2245559 | 0.1303889 | 0.0586711 | -0.1037955 | 0.0838571 | 0.0305541 | 0.2296280 | -0.3276759 | -0.0497649 | 0.2226552 | 0.3011864 | 0.2038148 | -0.1453653 | -0.4688105 | 0.7197390 | 0.2738403 | 0.0299769 | -0.1814475 | 0.0470294 | -0.0051320 | -0.0962845 | 0.2883311 | -0.1431770 | 1.0000000 | -0.0094178 | -0.0061092 | 0.4247910 | 0.0009563 | 0.0727186 | 0.0502825 | -0.1568370 | -0.1167335 | -0.5213116 | 0.0440477 | 0.0950253 | -0.2674318 | -0.0606495 | -0.2344827 | -0.2000574 | -0.0146892 | 0.0151738 | -0.0462261 | -0.0671038 | 0.0553753 | -0.0843594 | -0.0580498 | -0.2355274 | 0.1868627 | 0.0440707 | 0.1959890 | -0.4051392 | -0.1486051 | 0.1732249 | NA | -0.4125248 | 0.0904684 | -0.2032230 | -0.0429371 | 0.1824478 | -0.0037783 | -0.0368573 | -0.3100133 | 0.4154974 | -0.3732979 |
| -0.2505200 | 0.1291319 | -0.1010265 | -0.1671540 | -0.2159787 | 0.8793301 | -0.3607242 | -0.0722155 | 0.3070353 | 0.4259060 | -0.1296829 | -0.1180110 | 0.3465992 | 0.2194846 | 0.2300811 | -0.3049202 | 0.0954322 | -0.3116205 | -0.4678084 | 0.2965061 | -0.1368103 | 0.0663270 | -0.1958940 | 0.1294020 | -0.0703069 | -0.0840891 | -0.3653766 | 0.4238557 | -0.1795468 | 0.1085575 | -0.3765073 | 0.5450969 | -0.0421370 | -0.0094178 | 1.0000000 | 0.2502257 | 0.1759035 | -0.0943547 | -0.2846563 | -0.2289583 | -0.3180490 | -0.2336220 | -0.1678751 | -0.1835102 | 0.4959962 | 0.3852296 | 0.0957148 | 0.4162022 | -0.4167589 | 0.0554386 | 0.3290820 | 0.5160777 | -0.3035366 | -0.2800524 | -0.3263066 | -0.4677475 | -0.0962891 | -0.1273056 | -0.3484076 | -0.1408303 | -0.2175050 | -0.1700874 | 0.0858681 | NA | 0.3413376 | -0.1723645 | -0.4018072 | 0.1294766 | -0.0126270 | 0.3050679 | -0.1744413 | -0.2058999 | 0.2526991 | -0.1236512 |
| 0.1400971 | -0.0897263 | -0.0021907 | 0.5385248 | 0.0252439 | 0.1949485 | 0.3045213 | 0.3958766 | -0.4492692 | 0.2650904 | 0.0130632 | 0.3897107 | 0.0328022 | -0.2001576 | -0.2110288 | 0.2478162 | 0.3766221 | -0.1471417 | 0.1654104 | -0.3399369 | -0.0016023 | -0.6356143 | 0.1609997 | 0.1606493 | -0.1899341 | 0.2488668 | 0.2354991 | 0.0500750 | -0.2242048 | 0.2217156 | 0.2157655 | -0.0956674 | -0.0996884 | -0.0061092 | 0.2502257 | 1.0000000 | 0.4857479 | 0.1247275 | 0.2807903 | 0.3129763 | -0.0400249 | 0.2174444 | -0.1300877 | 0.2379719 | -0.1759160 | 0.0245966 | 0.4312685 | 0.1769668 | 0.1927633 | -0.0757770 | -0.0761981 | -0.0287151 | 0.2987558 | 0.2812007 | -0.0607028 | 0.0925388 | 0.2975955 | 0.2623610 | -0.0172720 | 0.4228189 | 0.2713266 | 0.5235792 | 0.2636439 | NA | 0.1068677 | -0.4252339 | 0.1465693 | -0.0285792 | 0.1280101 | 0.0309640 | 0.1269461 | 0.3765679 | -0.2042609 | -0.0903664 |
| -0.0343477 | 0.2778291 | 0.3546066 | 0.3175438 | 0.1938379 | 0.0948393 | 0.1865584 | 0.1987950 | -0.1802400 | 0.0797730 | -0.1158728 | 0.0329693 | 0.0378419 | -0.2755028 | -0.1942712 | 0.0891452 | 0.0957682 | -0.1706600 | 0.0356509 | -0.1164153 | 0.0845338 | -0.3317551 | -0.0206741 | 0.0883187 | 0.3392892 | 0.4359226 | 0.1255773 | -0.2062661 | -0.3308251 | -0.0506113 | 0.0154320 | 0.1595787 | -0.0128635 | 0.4247910 | 0.1759035 | 0.4857479 | 1.0000000 | 0.0262013 | 0.1622897 | -0.0245220 | 0.1195242 | 0.2245242 | -0.1241246 | 0.4048331 | 0.0056744 | -0.1509714 | 0.5388903 | 0.0856434 | 0.1430304 | 0.0975599 | 0.0319403 | 0.0263795 | 0.1768020 | 0.2669294 | 0.1137902 | -0.0701556 | 0.1175731 | 0.0142370 | 0.1808905 | 0.2222633 | -0.0160895 | 0.3160382 | 0.2530440 | NA | -0.0752923 | -0.2606155 | -0.0439148 | -0.0787116 | 0.3887465 | 0.0709506 | -0.0875100 | 0.1691842 | 0.1290065 | -0.1108564 |
| -0.0959208 | -0.0548264 | -0.1587063 | 0.3284598 | 0.3579924 | -0.3113629 | 0.3171844 | -0.0324993 | -0.5220758 | -0.1087953 | 0.1499706 | -0.0926059 | -0.1617256 | -0.2968019 | -0.4185976 | 0.2052858 | -0.0251399 | -0.0201533 | 0.4650862 | -0.4794566 | 0.1210991 | -0.3111788 | 0.2007336 | -0.2208636 | -0.0874288 | 0.2785948 | 0.3350186 | 0.0656344 | 0.0935163 | -0.0250539 | 0.2004957 | -0.4410488 | 0.2309243 | 0.0009563 | -0.0943547 | 0.1247275 | 0.0262013 | 1.0000000 | 0.4039829 | -0.1338665 | -0.1250390 | 0.2258826 | -0.1308439 | 0.5392884 | -0.4861949 | -0.0471231 | 0.2974479 | -0.0469057 | 0.0176632 | -0.3921215 | -0.2502321 | -0.5375967 | 0.2963495 | 0.3468922 | -0.1632574 | 0.3754966 | 0.5883942 | 0.0272205 | -0.1330274 | 0.3299650 | -0.1497429 | 0.3393645 | 0.3471363 | NA | -0.1346415 | -0.0459651 | 0.3268841 | -0.2384653 | -0.2539913 | -0.2359034 | 0.0159297 | -0.0083953 | -0.1549324 | 0.0461130 |
| 0.5034699 | 0.1257392 | 0.2322774 | 0.6653062 | 0.6416353 | -0.4751558 | 0.6483504 | 0.3841223 | -0.7276176 | -0.1143103 | 0.1646980 | 0.4283628 | -0.4283797 | -0.7163866 | -0.3767206 | 0.4638965 | 0.2466264 | 0.2603553 | 0.7880123 | -0.6474278 | 0.1202582 | -0.5382990 | 0.0390161 | 0.0809412 | 0.0960324 | 0.7785524 | 0.2767831 | -0.0091151 | -0.2720502 | 0.1496683 | 0.8001254 | -0.6107087 | -0.2431083 | 0.0727186 | -0.2846563 | 0.2807903 | 0.1622897 | 0.4039829 | 1.0000000 | 0.4495181 | 0.2850054 | 0.3818691 | 0.0886167 | 0.3039607 | -0.7591858 | 0.0351550 | 0.6139488 | -0.0618574 | 0.3626048 | -0.0087354 | -0.2785718 | -0.6972011 | 0.7058573 | 0.7617142 | 0.3113487 | 0.5165491 | 0.5893096 | 0.4142481 | 0.4299954 | 0.4553220 | 0.3129632 | 0.6753929 | 0.5982823 | NA | 0.0311228 | -0.3598364 | 0.6176411 | -0.3670350 | 0.3871837 | -0.4701638 | 0.0382020 | 0.2274687 | -0.3468863 | 0.0429263 |
| 0.8398009 | 0.3007155 | -0.1681559 | 0.4356759 | 0.0127316 | -0.2297294 | 0.7190193 | 0.6016592 | -0.3658601 | -0.0662241 | 0.0765113 | 0.9373167 | -0.3455713 | -0.2579619 | -0.3064042 | 0.2229799 | 0.6411761 | 0.3911630 | 0.3335478 | -0.2210211 | 0.1792057 | -0.3903563 | 0.1888297 | 0.2382741 | 0.2801136 | 0.4056423 | 0.1154399 | -0.0049249 | -0.1060237 | 0.0041606 | 0.6502469 | -0.3073988 | -0.3504500 | 0.0502825 | -0.2289583 | 0.3129763 | -0.0245220 | -0.1338665 | 0.4495181 | 1.0000000 | 0.0571954 | 0.1295855 | 0.2062129 | 0.0406139 | -0.3374324 | -0.0695262 | 0.1352855 | -0.0134511 | 0.3495726 | 0.3974122 | -0.1439098 | -0.2390103 | 0.4314899 | 0.3478781 | 0.1237754 | 0.4250594 | 0.1202789 | 0.5539313 | 0.3374610 | 0.3072958 | 0.3356389 | 0.4266157 | 0.0299704 | NA | 0.1563713 | -0.2333627 | 0.4335010 | 0.0437933 | 0.1381970 | -0.4111546 | 0.0327847 | 0.2706410 | -0.2876959 | -0.0031158 |
| 0.0749452 | -0.0716891 | 0.4955146 | 0.2637485 | 0.4042621 | -0.1817210 | 0.1803913 | 0.0329782 | -0.1526348 | 0.0556113 | 0.2327292 | -0.0315523 | -0.4415720 | -0.0366773 | -0.2178867 | 0.0763359 | -0.1674202 | 0.1960919 | 0.1252229 | -0.1860701 | -0.0847555 | -0.2233152 | -0.2328947 | 0.1736064 | -0.0442060 | 0.2063537 | -0.0523073 | -0.1199267 | -0.1478548 | 0.0346997 | 0.3652139 | -0.2133373 | -0.0690165 | -0.1568370 | -0.3180490 | -0.0400249 | 0.1195242 | -0.1250390 | 0.2850054 | 0.0571954 | 1.0000000 | 0.4169683 | 0.1977460 | -0.0474272 | -0.1205050 | -0.0654575 | 0.1320776 | -0.2584126 | 0.4604108 | 0.1318101 | -0.1906046 | -0.1930426 | 0.3601637 | 0.4079046 | 0.9873618 | 0.0548977 | 0.1260646 | 0.2348509 | 0.8814381 | -0.0041405 | 0.2994278 | 0.2776411 | 0.2406300 | NA | 0.0413501 | -0.1835295 | 0.1672540 | -0.3160107 | 0.3638499 | -0.2596907 | -0.1501779 | 0.3942464 | -0.2074087 | 0.2240400 |
| 0.1044471 | 0.0346474 | 0.3534044 | 0.4644739 | 0.4181547 | -0.2331957 | 0.4329089 | 0.4669722 | -0.5423189 | 0.0996401 | 0.0735662 | 0.1220141 | -0.5270410 | -0.3201999 | -0.3195130 | 0.2055034 | 0.0890478 | 0.2535280 | 0.3925554 | -0.5117192 | 0.2750373 | -0.3751983 | -0.0309244 | 0.2229608 | 0.0098730 | 0.3294871 | 0.1231979 | 0.0513219 | -0.2049542 | -0.0496045 | 0.3993858 | -0.3947869 | -0.1801972 | -0.1167335 | -0.2336220 | 0.2174444 | 0.2245242 | 0.2258826 | 0.3818691 | 0.1295855 | 0.4169683 | 1.0000000 | 0.3008134 | 0.3143775 | -0.3884923 | 0.0209355 | 0.4824030 | 0.0395983 | 0.7686370 | -0.0051810 | -0.1519092 | -0.4709748 | 0.6435640 | 0.7210880 | 0.3672427 | 0.1617605 | 0.4981069 | 0.1831782 | 0.4225678 | 0.2700279 | 0.5287502 | 0.4707222 | 0.3545819 | NA | 0.1524603 | -0.3329163 | 0.3915843 | -0.1072968 | 0.3487999 | -0.4340136 | 0.0735449 | 0.5052520 | -0.3481542 | 0.2728574 |
| 0.3933478 | -0.3058716 | -0.1654599 | 0.0500288 | 0.1671658 | -0.0344347 | 0.1012627 | 0.4325527 | -0.1073301 | 0.2813457 | -0.2506728 | 0.1242312 | -0.2205896 | -0.0715942 | -0.3150168 | 0.0020757 | -0.0190532 | 0.8246578 | 0.0793307 | -0.1440696 | -0.0706407 | -0.0214997 | 0.0641757 | 0.6157970 | -0.2822397 | 0.0063242 | -0.0335690 | -0.0337282 | -0.2125934 | -0.1240335 | 0.3479853 | -0.2442398 | -0.0971172 | -0.5213116 | -0.1678751 | -0.1300877 | -0.1241246 | -0.1308439 | 0.0886167 | 0.2062129 | 0.1977460 | 0.3008134 | 1.0000000 | 0.1393168 | -0.0910201 | -0.0324462 | 0.2369319 | 0.3492345 | 0.3445442 | 0.4375037 | -0.0881563 | -0.0605150 | 0.2306352 | 0.1633765 | 0.1390114 | 0.2493348 | 0.2022430 | -0.2414892 | 0.0647326 | 0.0069999 | 0.6671485 | 0.0529445 | 0.0301319 | NA | 0.3937932 | -0.3035678 | 0.3927158 | 0.1337550 | -0.1136109 | -0.2099831 | 0.1030904 | 0.3492686 | -0.3812995 | 0.4316053 |
| 0.2070382 | 0.1285247 | 0.2212147 | 0.3679043 | 0.4001700 | -0.2212843 | 0.4692360 | 0.3156283 | -0.4932327 | 0.0598588 | 0.1213397 | 0.0559811 | -0.1030682 | -0.2963703 | -0.5787747 | 0.2956022 | 0.0487445 | 0.2018123 | 0.3158118 | -0.3276132 | 0.3448085 | -0.3354372 | 0.2862006 | -0.0137419 | 0.1326255 | 0.4489461 | 0.5608042 | 0.0603137 | -0.0857176 | -0.1662495 | 0.3865486 | -0.1415782 | 0.0681386 | 0.0440477 | -0.1835102 | 0.2379719 | 0.4048331 | 0.5392884 | 0.3039607 | 0.0406139 | -0.0474272 | 0.3143775 | 0.1393168 | 1.0000000 | -0.3250125 | -0.0617801 | 0.4333563 | 0.1493722 | 0.2176817 | 0.1371705 | -0.3911011 | -0.4753882 | 0.4554908 | 0.5065084 | -0.0945621 | 0.5829454 | 0.3918398 | 0.0595816 | -0.1008895 | 0.4924946 | 0.1229946 | 0.3749421 | 0.3858192 | NA | -0.0067728 | -0.0714321 | 0.4523782 | -0.0096263 | 0.1769045 | -0.1739496 | 0.0347554 | 0.0886386 | -0.4126114 | 0.2920071 |
| -0.3530780 | 0.1665054 | 0.0157065 | -0.4986410 | -0.3229484 | 0.7420387 | -0.5909248 | -0.2042296 | 0.7942448 | 0.4201767 | 0.0102230 | -0.2019965 | 0.2407797 | 0.7326184 | 0.3257298 | -0.3495871 | 0.0711631 | -0.1984171 | -0.9900159 | 0.7755694 | -0.0690701 | 0.4119016 | -0.2826244 | -0.0716961 | 0.1531684 | -0.4874915 | -0.3212710 | 0.1081047 | -0.0793357 | 0.0856324 | -0.6019803 | 0.8546539 | -0.1353150 | 0.0950253 | 0.4959962 | -0.1759160 | 0.0056744 | -0.4861949 | -0.7591858 | -0.3374324 | -0.1205050 | -0.3884923 | -0.0910201 | -0.3250125 | 1.0000000 | -0.0245921 | -0.4580046 | 0.1420890 | -0.4444733 | 0.2253288 | 0.3499320 | 0.7644492 | -0.6386567 | -0.6184338 | -0.1188989 | -0.4880503 | -0.5523928 | -0.3066280 | -0.2177279 | -0.3790725 | -0.2855586 | -0.5074866 | -0.2125474 | NA | 0.0316736 | 0.1904320 | -0.6422888 | 0.2589046 | -0.0318145 | 0.2527181 | -0.2860123 | -0.3421594 | 0.3508309 | -0.0390594 |
| -0.0320244 | 0.0680778 | 0.0866925 | -0.0470497 | 0.0747764 | 0.3639310 | -0.1423458 | -0.0881063 | -0.0674455 | 0.2187524 | -0.1462499 | -0.0946356 | 0.1013428 | 0.0491496 | 0.0070820 | -0.0336769 | -0.1048014 | -0.0826300 | -0.0016183 | 0.0290645 | 0.0322620 | 0.0759500 | -0.0581922 | 0.2562957 | -0.2326516 | 0.0027889 | -0.1823801 | 0.8211721 | 0.0628184 | -0.0544381 | -0.0117338 | 0.0165651 | 0.0658866 | -0.2674318 | 0.3852296 | 0.0245966 | -0.1509714 | -0.0471231 | 0.0351550 | -0.0695262 | -0.0654575 | 0.0209355 | -0.0324462 | -0.0617801 | -0.0245921 | 1.0000000 | -0.0195396 | 0.0022399 | -0.0708211 | -0.0159664 | 0.1381384 | 0.0741953 | 0.0004650 | 0.0332675 | -0.0942382 | -0.0888519 | -0.0205449 | 0.1017313 | -0.0544868 | 0.1579273 | -0.0782036 | -0.0402407 | 0.2144059 | NA | 0.6750336 | -0.0758747 | -0.0223061 | 0.2040044 | 0.2123422 | 0.0912287 | 0.1919677 | -0.1466046 | -0.0434705 | 0.0459669 |
| 0.3017633 | 0.0580267 | 0.1447740 | 0.6364930 | 0.4088930 | -0.1105000 | 0.3885699 | 0.4222412 | -0.4870471 | -0.0683962 | -0.1030098 | 0.1304505 | -0.3735566 | -0.5941878 | -0.3378127 | 0.3375962 | 0.0572960 | 0.1126348 | 0.5046739 | -0.5719020 | -0.0904409 | -0.5978872 | -0.1014671 | 0.3937564 | 0.0307099 | 0.5601885 | 0.3248794 | -0.0130207 | -0.5319130 | 0.2069887 | 0.4977046 | -0.3562744 | -0.1103329 | -0.0606495 | 0.0957148 | 0.4312685 | 0.5388903 | 0.2974479 | 0.6139488 | 0.1352855 | 0.1320776 | 0.4824030 | 0.2369319 | 0.4333563 | -0.4580046 | -0.0195396 | 1.0000000 | 0.2272216 | 0.4895582 | 0.0588026 | -0.0771861 | -0.3580658 | 0.6248488 | 0.6088375 | 0.1012433 | 0.2743911 | 0.7584343 | -0.0312414 | 0.1508759 | 0.2186180 | 0.4567093 | 0.6425167 | 0.3020075 | NA | 0.2119448 | -0.3427822 | 0.5124552 | -0.4402265 | 0.3014163 | -0.3683541 | -0.1788735 | 0.4698066 | -0.2641353 | 0.1421191 |
| 0.1452748 | -0.2422309 | -0.1069830 | -0.0838046 | -0.1964753 | 0.3592424 | -0.0236401 | 0.4718722 | -0.0503673 | 0.3909316 | 0.0573155 | 0.0317821 | 0.2075380 | -0.0953373 | 0.2084531 | 0.0424680 | 0.0945040 | 0.1813207 | -0.1150459 | 0.0901896 | 0.2470792 | -0.0270687 | 0.0388726 | 0.2083639 | -0.3084214 | 0.0590597 | 0.0456819 | 0.0017223 | -0.0757172 | 0.0092503 | -0.0899425 | 0.2262591 | -0.0843730 | -0.2344827 | 0.4162022 | 0.1769668 | 0.0856434 | -0.0469057 | -0.0618574 | -0.0134511 | -0.2584126 | 0.0395983 | 0.3492345 | 0.1493722 | 0.1420890 | 0.0022399 | 0.2272216 | 1.0000000 | -0.1399067 | 0.0657646 | -0.1191782 | 0.0500206 | -0.0672379 | 0.0649558 | -0.2594793 | -0.0618379 | 0.1636131 | -0.2810627 | -0.3424107 | 0.2512675 | 0.2641150 | -0.0943227 | 0.0587090 | NA | 0.1434819 | -0.3433527 | 0.0442496 | 0.3610106 | -0.1011977 | 0.1883918 | 0.1778815 | 0.0394411 | -0.0642934 | -0.0784239 |
| 0.3336605 | -0.0687146 | 0.2082495 | 0.5588319 | 0.2647309 | -0.3636272 | 0.5070108 | 0.4453646 | -0.4665881 | -0.1077815 | 0.0863102 | 0.2386778 | -0.5860381 | -0.4103231 | -0.4150858 | 0.2949266 | 0.0013797 | 0.3977596 | 0.4562404 | -0.5466055 | 0.0596690 | -0.4660248 | 0.0159557 | 0.2419268 | -0.0108591 | 0.2159009 | 0.2872742 | -0.0986635 | -0.2559915 | 0.0810734 | 0.5016640 | -0.4888212 | -0.1441134 | -0.2000574 | -0.4167589 | 0.1927633 | 0.1430304 | 0.0176632 | 0.3626048 | 0.3495726 | 0.4604108 | 0.7686370 | 0.3445442 | 0.2176817 | -0.4444733 | -0.0708211 | 0.4895582 | -0.1399067 | 1.0000000 | 0.1727717 | -0.1463058 | -0.4300802 | 0.6878799 | 0.6273329 | 0.4306075 | 0.3473053 | 0.4959179 | 0.1171506 | 0.5210565 | 0.1546916 | 0.5780723 | 0.5613766 | 0.0281198 | NA | 0.0708333 | -0.0350513 | 0.5211730 | -0.1384014 | 0.2626842 | -0.4802491 | -0.0723979 | 0.7274366 | -0.4332333 | 0.3522578 |
| 0.5465737 | 0.3654918 | -0.0075445 | -0.0018173 | 0.0879980 | 0.1954313 | 0.2721160 | 0.3654745 | 0.2227805 | 0.2112073 | -0.1184203 | 0.3363288 | -0.1056313 | 0.0594236 | -0.2788321 | -0.1432439 | 0.2041206 | 0.4103786 | -0.2059236 | 0.2874081 | -0.0085455 | 0.0942628 | -0.2522827 | 0.3918809 | 0.3972309 | 0.2464470 | -0.0996743 | 0.1423610 | -0.2612465 | -0.2289943 | 0.3580457 | 0.3084766 | -0.5466721 | -0.0146892 | 0.0554386 | -0.0757770 | 0.0975599 | -0.3921215 | -0.0087354 | 0.3974122 | 0.1318101 | -0.0051810 | 0.4375037 | 0.1371705 | 0.2253288 | -0.0159664 | 0.0588026 | 0.0657646 | 0.1727717 | 1.0000000 | 0.0725983 | 0.2407805 | 0.1339186 | 0.0456957 | 0.1499787 | 0.2471552 | -0.0909200 | 0.1125909 | 0.2190808 | -0.0451525 | 0.3031644 | -0.0028235 | -0.0071956 | NA | 0.3490250 | -0.1227070 | 0.1587950 | -0.0041594 | 0.1783958 | -0.1263298 | -0.3181458 | 0.1327757 | -0.4871924 | 0.6249405 |
| -0.1284160 | 0.1550968 | -0.2814987 | 0.0411577 | -0.0061626 | 0.3216523 | -0.3947974 | -0.3173311 | 0.4022458 | 0.0360748 | -0.2455995 | -0.1600498 | 0.0296906 | 0.1879401 | 0.2827438 | -0.4727844 | -0.1122097 | -0.2954252 | -0.3241863 | 0.1056014 | -0.4100273 | -0.0208003 | -0.3718132 | 0.2929515 | 0.0594024 | -0.2571017 | -0.4870366 | 0.1873388 | -0.4043290 | -0.1359580 | -0.3274796 | 0.2652950 | 0.0035245 | 0.0151738 | 0.3290820 | -0.0761981 | 0.0319403 | -0.2502321 | -0.2785718 | -0.1439098 | -0.1906046 | -0.1519092 | -0.0881563 | -0.3911011 | 0.3499320 | 0.1381384 | -0.0771861 | -0.1191782 | -0.1463058 | 0.0725983 | 1.0000000 | 0.4010382 | -0.2367254 | -0.4486269 | -0.1936272 | -0.3514496 | -0.1844078 | -0.2593477 | -0.0771643 | -0.5889188 | -0.2390401 | 0.0374709 | -0.1117559 | NA | 0.2994468 | 0.0912464 | -0.3873870 | -0.1409263 | 0.0525889 | 0.0157609 | -0.3767530 | 0.0211469 | 0.2758113 | -0.0449332 |
| -0.3552700 | -0.0053645 | -0.2048827 | -0.4237210 | -0.4552528 | 0.6707114 | -0.5788920 | -0.3294636 | 0.8633454 | 0.2795003 | -0.4020967 | -0.1446451 | 0.4105682 | 0.6709072 | 0.3144671 | -0.3660578 | 0.0449012 | -0.2628289 | -0.7864103 | 0.7184968 | -0.4209381 | 0.3456279 | -0.2732411 | 0.1593078 | -0.0071639 | -0.5281817 | -0.3804133 | 0.1218197 | -0.0494816 | 0.1597178 | -0.5723226 | 0.6017271 | -0.0581169 | -0.0462261 | 0.5160777 | -0.0287151 | 0.0263795 | -0.5375967 | -0.6972011 | -0.2390103 | -0.1930426 | -0.4709748 | -0.0605150 | -0.4753882 | 0.7644492 | 0.0741953 | -0.3580658 | 0.0500206 | -0.4300802 | 0.2407805 | 0.4010382 | 1.0000000 | -0.6749324 | -0.7334201 | -0.2098607 | -0.5332975 | -0.4890402 | -0.2097869 | -0.2811685 | -0.5390298 | -0.2119448 | -0.4322758 | -0.3843973 | NA | 0.1921555 | 0.0359629 | -0.6168892 | 0.1706188 | -0.1533037 | 0.4026679 | -0.2205197 | -0.2018372 | 0.3384805 | -0.0728476 |
| 0.5325194 | 0.1392709 | 0.2536023 | 0.7344790 | 0.6300564 | -0.4015080 | 0.6767919 | 0.4685761 | -0.6878439 | -0.0205297 | -0.0234283 | 0.3827430 | -0.5605206 | -0.5935588 | -0.4883014 | 0.1300495 | 0.2247050 | 0.3224972 | 0.6568487 | -0.6305675 | 0.0862356 | -0.6338506 | -0.0289622 | 0.1910259 | 0.0999760 | 0.5837188 | 0.1685687 | 0.0186128 | -0.3562606 | -0.1458259 | 0.8428192 | -0.5591793 | -0.1001514 | -0.0671038 | -0.3035366 | 0.2987558 | 0.1768020 | 0.2963495 | 0.7058573 | 0.4314899 | 0.3601637 | 0.6435640 | 0.2306352 | 0.4554908 | -0.6386567 | 0.0004650 | 0.6248488 | -0.0672379 | 0.6878799 | 0.1339186 | -0.2367254 | -0.6749324 | 1.0000000 | 0.8822205 | 0.3475288 | 0.5173709 | 0.6019578 | 0.3247849 | 0.4447514 | 0.3765403 | 0.5184121 | 0.7385187 | 0.5192691 | NA | 0.1231073 | -0.1601594 | 0.6983632 | -0.2883031 | 0.3589719 | -0.5431335 | -0.0955137 | 0.4251471 | -0.4697215 | 0.2683181 |
| 0.4411611 | 0.1600551 | 0.4657144 | 0.6053722 | 0.6129884 | -0.3947446 | 0.7041850 | 0.5775344 | -0.7400500 | 0.0079828 | 0.1809886 | 0.3487747 | -0.5152761 | -0.6485240 | -0.3868160 | 0.3744051 | 0.2511617 | 0.2912273 | 0.6514192 | -0.5606912 | 0.4205461 | -0.5225317 | -0.0150426 | 0.0476063 | 0.1235547 | 0.6912965 | 0.2902280 | 0.0269827 | -0.1809822 | -0.0168519 | 0.7461404 | -0.4818549 | -0.1655737 | 0.0553753 | -0.2800524 | 0.2812007 | 0.2669294 | 0.3468922 | 0.7617142 | 0.3478781 | 0.4079046 | 0.7210880 | 0.1633765 | 0.5065084 | -0.6184338 | 0.0332675 | 0.6088375 | 0.0649558 | 0.6273329 | 0.0456957 | -0.4486269 | -0.7334201 | 0.8822205 | 1.0000000 | 0.4115302 | 0.5024762 | 0.5862371 | 0.3175988 | 0.4826414 | 0.5590682 | 0.4529395 | 0.6105860 | 0.6059804 | NA | 0.0351019 | -0.2726676 | 0.6586023 | -0.1489159 | 0.4692248 | -0.4938980 | 0.1208411 | 0.2983288 | -0.4549956 | 0.2193141 |
| 0.1356697 | -0.0029766 | 0.5044028 | 0.2495990 | 0.3814426 | -0.2044245 | 0.2118292 | 0.0599776 | -0.1455400 | 0.0056721 | 0.2772163 | 0.0355122 | -0.4636460 | -0.0562270 | -0.1614265 | 0.0856362 | -0.1124543 | 0.1681366 | 0.1310120 | -0.1490489 | -0.0237314 | -0.2053035 | -0.2520481 | 0.1324115 | 0.0342844 | 0.2516651 | -0.0553076 | -0.1567471 | -0.1385873 | 0.0313521 | 0.3712445 | -0.1813364 | -0.0954496 | -0.0843594 | -0.3263066 | -0.0607028 | 0.1137902 | -0.1632574 | 0.3113487 | 0.1237754 | 0.9873618 | 0.3672427 | 0.1390114 | -0.0945621 | -0.1188989 | -0.0942382 | 0.1012433 | -0.2594793 | 0.4306075 | 0.1499787 | -0.1936272 | -0.2098607 | 0.3475288 | 0.4115302 | 1.0000000 | 0.0531825 | 0.0787255 | 0.2810665 | 0.9212356 | 0.0186034 | 0.2531851 | 0.2622343 | 0.2261886 | NA | -0.0045749 | -0.1616789 | 0.1492574 | -0.3007876 | 0.3973442 | -0.2872064 | -0.1556881 | 0.3595096 | -0.1842491 | 0.1828912 |
| 0.6089168 | 0.0853344 | -0.1228004 | 0.4492479 | 0.4652115 | -0.4706342 | 0.7052274 | 0.3885533 | -0.4588109 | -0.1940021 | 0.2239724 | 0.3734712 | -0.2223204 | -0.4257810 | -0.5544185 | 0.3871774 | 0.1781163 | 0.3834756 | 0.4829773 | -0.3071201 | 0.1610899 | -0.3864678 | 0.2275847 | 0.0178214 | 0.0948435 | 0.3915041 | 0.4626031 | 0.0095255 | 0.1369924 | 0.0276635 | 0.6325513 | -0.4508752 | -0.1453015 | -0.0580498 | -0.4677475 | 0.0925388 | -0.0701556 | 0.3754966 | 0.5165491 | 0.4250594 | 0.0548977 | 0.1617605 | 0.2493348 | 0.5829454 | -0.4880503 | -0.0888519 | 0.2743911 | -0.0618379 | 0.3473053 | 0.2471552 | -0.3514496 | -0.5332975 | 0.5173709 | 0.5024762 | 0.0531825 | 1.0000000 | 0.5199791 | 0.1677212 | 0.1237797 | 0.4151186 | 0.3318362 | 0.4474561 | 0.2567187 | NA | 0.0006667 | 0.2223949 | 0.8657241 | -0.1589717 | -0.0322612 | -0.2957721 | 0.1379181 | 0.1002012 | -0.4508286 | 0.3021062 |
| 0.2909441 | -0.0568803 | -0.1047375 | 0.5847069 | 0.3686684 | -0.2918482 | 0.5266034 | 0.4033742 | -0.5208901 | -0.1467801 | 0.0680947 | 0.0917993 | -0.4128226 | -0.5050610 | -0.4398382 | 0.4166113 | -0.0016262 | 0.0921268 | 0.5669557 | -0.5805838 | -0.0840316 | -0.5528326 | -0.0296593 | 0.2935304 | -0.1228993 | 0.3278679 | 0.4204615 | 0.0920492 | -0.1998038 | 0.2414599 | 0.5128824 | -0.5565374 | -0.0816062 | -0.2355274 | -0.0962891 | 0.2975955 | 0.1175731 | 0.5883942 | 0.5893096 | 0.1202789 | 0.1260646 | 0.4981069 | 0.2022430 | 0.3918398 | -0.5523928 | -0.0205449 | 0.7584343 | 0.1636131 | 0.4959179 | -0.0909200 | -0.1844078 | -0.4890402 | 0.6019578 | 0.5862371 | 0.0787255 | 0.5199791 | 1.0000000 | 0.0180525 | 0.1007754 | 0.2748850 | 0.5072681 | 0.5896248 | 0.2348489 | NA | 0.2043235 | -0.1688542 | 0.7257946 | -0.4817751 | -0.0354767 | -0.4071294 | -0.0552895 | 0.4829634 | -0.3578762 | 0.2339716 |
| 0.2816119 | 0.3690220 | 0.2413263 | 0.2712789 | 0.1629651 | -0.1757736 | 0.5268679 | 0.1557469 | -0.3043445 | 0.0216271 | 0.0896419 | 0.5566360 | -0.2460695 | 0.0356312 | -0.2677984 | 0.1382459 | 0.4486630 | -0.0656917 | 0.2575160 | -0.0880986 | 0.0928023 | -0.1575724 | 0.1595086 | -0.0096758 | 0.3489414 | 0.3512996 | 0.0576254 | 0.0805062 | 0.0181695 | -0.0138517 | 0.4609837 | -0.1876936 | -0.1856198 | 0.1868627 | -0.1273056 | 0.2623610 | 0.0142370 | 0.0272205 | 0.4142481 | 0.5539313 | 0.2348509 | 0.1831782 | -0.2414892 | 0.0595816 | -0.3066280 | 0.1017313 | -0.0312414 | -0.2810627 | 0.1171506 | 0.1125909 | -0.2593477 | -0.2097869 | 0.3247849 | 0.3175988 | 0.2810665 | 0.1677212 | 0.0180525 | 1.0000000 | 0.3847424 | 0.3557813 | 0.0569257 | 0.2769125 | 0.3200881 | NA | 0.0363574 | -0.2076185 | 0.1779706 | -0.1443119 | 0.3706249 | -0.2472059 | 0.0859054 | 0.0696636 | -0.0674496 | -0.1363763 |
| 0.2995470 | 0.1808126 | 0.4472444 | 0.4191249 | 0.4388754 | -0.2731519 | 0.4078620 | 0.1524749 | -0.2291549 | -0.0969316 | 0.2220848 | 0.2471154 | -0.5337813 | -0.2088553 | -0.2097430 | 0.0905792 | 0.0561350 | 0.1831879 | 0.2428026 | -0.2276850 | 0.0432006 | -0.3340775 | -0.1994929 | 0.1237047 | 0.2202618 | 0.3920549 | -0.0906596 | -0.1434127 | -0.1745886 | -0.0341942 | 0.4677771 | -0.2425653 | -0.2013727 | 0.0440707 | -0.3484076 | -0.0172720 | 0.1808905 | -0.1330274 | 0.4299954 | 0.3374610 | 0.8814381 | 0.4225678 | 0.0647326 | -0.1008895 | -0.2177279 | -0.0544868 | 0.1508759 | -0.3424107 | 0.5210565 | 0.2190808 | -0.0771643 | -0.2811685 | 0.4447514 | 0.4826414 | 0.9212356 | 0.1237797 | 0.1007754 | 0.3847424 | 1.0000000 | 0.0570358 | 0.1648624 | 0.4286379 | 0.2442132 | NA | -0.0004668 | -0.1263941 | 0.2018754 | -0.2713137 | 0.4678883 | -0.3901615 | -0.1787915 | 0.3425353 | -0.1759720 | 0.1449397 |
| 0.3047038 | 0.0869484 | 0.1996859 | 0.1843016 | 0.1953624 | -0.1497582 | 0.4685699 | 0.5443837 | -0.6817372 | 0.2167861 | 0.3211876 | 0.3568623 | -0.0834071 | -0.3417552 | -0.3125551 | 0.4100502 | 0.3275437 | 0.1744106 | 0.3655689 | -0.1518816 | 0.5639013 | -0.1778663 | 0.2392858 | -0.1022451 | 0.0364759 | 0.4635453 | 0.4508235 | 0.0624946 | 0.1650045 | -0.0266873 | 0.3493955 | -0.1410853 | -0.0993249 | 0.1959890 | -0.1408303 | 0.4228189 | 0.2222633 | 0.3299650 | 0.4553220 | 0.3072958 | -0.0041405 | 0.2700279 | 0.0069999 | 0.4924946 | -0.3790725 | 0.1579273 | 0.2186180 | 0.2512675 | 0.1546916 | -0.0451525 | -0.5889188 | -0.5390298 | 0.3765403 | 0.5590682 | 0.0186034 | 0.4151186 | 0.2748850 | 0.3557813 | 0.0570358 | 1.0000000 | 0.1986483 | 0.1790963 | 0.4647929 | NA | -0.0267944 | -0.2333849 | 0.4123268 | 0.1961525 | 0.1392599 | -0.1087048 | 0.4695141 | 0.0463247 | -0.2315824 | -0.0212822 |
| 0.4708363 | -0.1367618 | -0.0255151 | 0.2601249 | 0.1509763 | -0.1498797 | 0.3445936 | 0.6544855 | -0.3366015 | 0.1388448 | -0.0709309 | 0.2538221 | -0.3641106 | -0.1988647 | -0.3300379 | 0.2150135 | 0.0670752 | 0.4523422 | 0.2750728 | -0.3187638 | -0.0521085 | -0.2279243 | -0.0574080 | 0.5651296 | -0.1863237 | 0.1451303 | 0.1599666 | 0.0339178 | -0.2390063 | 0.0637338 | 0.5334115 | -0.3767710 | -0.2346339 | -0.4051392 | -0.2175050 | 0.2713266 | -0.0160895 | -0.1497429 | 0.3129632 | 0.3356389 | 0.2994278 | 0.5287502 | 0.6671485 | 0.1229946 | -0.2855586 | -0.0782036 | 0.4567093 | 0.2641150 | 0.5780723 | 0.3031644 | -0.2390401 | -0.2119448 | 0.5184121 | 0.4529395 | 0.2531851 | 0.3318362 | 0.5072681 | 0.0569257 | 0.1648624 | 0.1986483 | 1.0000000 | 0.2577240 | 0.0890226 | NA | 0.3800986 | -0.3530457 | 0.6011375 | -0.1369793 | 0.0511238 | -0.3052569 | 0.0771761 | 0.6369609 | -0.4862412 | 0.3757766 |
| 0.3926176 | 0.0426136 | 0.1503119 | 0.9992779 | 0.6093625 | -0.2589159 | 0.6052094 | 0.3200495 | -0.5467316 | -0.0538476 | -0.0245879 | 0.4403364 | -0.4514829 | -0.5163053 | -0.4790366 | 0.2774001 | 0.3146475 | 0.1590467 | 0.5252350 | -0.6707541 | -0.1283056 | -0.9463288 | 0.1258652 | 0.1925131 | 0.0024631 | 0.4943155 | 0.2436113 | -0.0403464 | -0.4872883 | 0.1630721 | 0.6665499 | -0.5003404 | -0.1366889 | -0.1486051 | -0.1700874 | 0.5235792 | 0.3160382 | 0.3393645 | 0.6753929 | 0.4266157 | 0.2776411 | 0.4707222 | 0.0529445 | 0.3749421 | -0.5074866 | -0.0402407 | 0.6425167 | -0.0943227 | 0.5613766 | -0.0028235 | 0.0374709 | -0.4322758 | 0.7385187 | 0.6105860 | 0.2622343 | 0.4474561 | 0.5896248 | 0.2769125 | 0.4286379 | 0.1790963 | 0.2577240 | 1.0000000 | 0.4321693 | NA | 0.0632446 | -0.1704194 | 0.4994896 | -0.3037145 | 0.3824376 | -0.4667609 | -0.2222804 | 0.4265132 | -0.3149523 | 0.0714459 |
| 0.0965646 | 0.1883283 | 0.4609757 | 0.4238636 | 0.8084308 | 0.0583089 | 0.2691203 | 0.1954710 | -0.4348922 | 0.5153256 | 0.0736338 | 0.1659461 | -0.1821533 | -0.2887744 | -0.2741626 | 0.0658406 | 0.3045819 | 0.1485570 | 0.2259505 | -0.2188365 | 0.1974954 | -0.3213795 | -0.0806868 | -0.0505621 | 0.0845592 | 0.5461311 | -0.0351940 | 0.1973807 | -0.2968178 | -0.0319428 | 0.4805674 | -0.1029378 | -0.1432002 | 0.1732249 | 0.0858681 | 0.2636439 | 0.2530440 | 0.3471363 | 0.5982823 | 0.0299704 | 0.2406300 | 0.3545819 | 0.0301319 | 0.3858192 | -0.2125474 | 0.2144059 | 0.3020075 | 0.0587090 | 0.0281198 | -0.0071956 | -0.1117559 | -0.3843973 | 0.5192691 | 0.6059804 | 0.2261886 | 0.2567187 | 0.2348489 | 0.3200881 | 0.2442132 | 0.4647929 | 0.0890226 | 0.4321693 | 1.0000000 | NA | 0.0889492 | -0.3005974 | 0.2428908 | -0.0474467 | 0.4985053 | -0.2269575 | 0.0594833 | -0.1339377 | -0.1162564 | 0.0349016 |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 0.2992647 | 0.1800174 | -0.1532263 | 0.0576323 | 0.0347825 | 0.3555468 | -0.0099663 | 0.2371869 | 0.0183993 | 0.2591986 | -0.3079177 | 0.0770348 | -0.1997099 | 0.1601441 | -0.1366338 | -0.0564609 | -0.0254117 | 0.0604424 | -0.0620973 | -0.0107608 | -0.1214939 | -0.0453409 | -0.2852008 | 0.8199013 | -0.1285389 | -0.0349296 | -0.1448905 | 0.7850273 | -0.2425506 | -0.1075011 | 0.2109969 | -0.0436212 | -0.0828695 | -0.4125248 | 0.3413376 | 0.1068677 | -0.0752923 | -0.1346415 | 0.0311228 | 0.1563713 | 0.0413501 | 0.1524603 | 0.3937932 | -0.0067728 | 0.0316736 | 0.6750336 | 0.2119448 | 0.1434819 | 0.0708333 | 0.3490250 | 0.2994468 | 0.1921555 | 0.1231073 | 0.0351019 | -0.0045749 | 0.0006667 | 0.2043235 | 0.0363574 | -0.0004668 | -0.0267944 | 0.3800986 | 0.0632446 | 0.0889492 | NA | 1.0000000 | -0.3955790 | 0.1339441 | -0.0556135 | 0.1226937 | -0.2205765 | -0.0201076 | 0.2586064 | -0.2757657 | 0.3369285 |
| -0.1549458 | 0.0364618 | -0.1484664 | -0.1655444 | -0.0225270 | -0.1059630 | -0.1259129 | -0.3907039 | 0.3133523 | -0.3858612 | 0.0772345 | -0.2576386 | 0.1675716 | 0.1104936 | 0.0444249 | -0.2267015 | -0.2250578 | -0.1496611 | -0.1843326 | 0.2635648 | -0.1326115 | 0.1859026 | 0.1349882 | -0.4685250 | 0.1310063 | -0.3161023 | -0.0228839 | -0.1757300 | 0.3390412 | -0.0574901 | -0.2960997 | 0.1182985 | 0.3018740 | 0.0904684 | -0.1723645 | -0.4252339 | -0.2606155 | -0.0459651 | -0.3598364 | -0.2333627 | -0.1835295 | -0.3329163 | -0.3035678 | -0.0714321 | 0.1904320 | -0.0758747 | -0.3427822 | -0.3433527 | -0.0350513 | -0.1227070 | 0.0912464 | 0.0359629 | -0.1601594 | -0.2726676 | -0.1616789 | 0.2223949 | -0.1688542 | -0.2076185 | -0.1263941 | -0.2333849 | -0.3530457 | -0.1704194 | -0.3005974 | NA | -0.3955790 | 1.0000000 | 0.0679364 | 0.0490921 | -0.2178315 | 0.1736432 | -0.1038571 | -0.3231911 | 0.3221512 | -0.1036240 |
| 0.6349779 | -0.0092149 | -0.1028728 | 0.5003933 | 0.4493481 | -0.4926264 | 0.7396206 | 0.5155409 | -0.5901055 | -0.2451190 | 0.0760772 | 0.3485024 | -0.2937644 | -0.5800599 | -0.5138709 | 0.3430234 | 0.1280014 | 0.4097318 | 0.6491214 | -0.4759947 | 0.0714990 | -0.4445034 | 0.2076236 | 0.2294265 | -0.0301929 | 0.4318080 | 0.3416510 | -0.0132911 | 0.0916729 | 0.0114366 | 0.7213911 | -0.6177578 | -0.1112623 | -0.2032230 | -0.4018072 | 0.1465693 | -0.0439148 | 0.3268841 | 0.6176411 | 0.4335010 | 0.1672540 | 0.3915843 | 0.3927158 | 0.4523782 | -0.6422888 | -0.0223061 | 0.5124552 | 0.0442496 | 0.5211730 | 0.1587950 | -0.3873870 | -0.6168892 | 0.6983632 | 0.6586023 | 0.1492574 | 0.8657241 | 0.7257946 | 0.1779706 | 0.2018754 | 0.4123268 | 0.6011375 | 0.4994896 | 0.2428908 | NA | 0.1339441 | 0.0679364 | 1.0000000 | -0.2757215 | -0.0220596 | -0.3047779 | 0.2162189 | 0.2802598 | -0.4440007 | 0.2848627 |
| -0.0433099 | -0.1545346 | 0.0938636 | -0.2943415 | -0.2992783 | 0.2673250 | -0.1286339 | 0.2159772 | 0.0958651 | 0.3350620 | -0.0555324 | 0.1098666 | 0.3502449 | 0.1260111 | 0.1093798 | 0.0063870 | 0.1803651 | 0.2675817 | -0.2687250 | 0.2587999 | 0.4896005 | 0.2370960 | 0.4478112 | -0.1732665 | -0.1625246 | -0.2719080 | -0.0165658 | 0.0491646 | 0.2455184 | -0.0672509 | -0.3034006 | 0.2950796 | 0.1782008 | -0.0429371 | 0.1294766 | -0.0285792 | -0.0787116 | -0.2384653 | -0.3670350 | 0.0437933 | -0.3160107 | -0.1072968 | 0.1337550 | -0.0096263 | 0.2589046 | 0.2040044 | -0.4402265 | 0.3610106 | -0.1384014 | -0.0041594 | -0.1409263 | 0.1706188 | -0.2883031 | -0.1489159 | -0.3007876 | -0.1589717 | -0.4817751 | -0.1443119 | -0.2713137 | 0.1961525 | -0.1369793 | -0.3037145 | -0.0474467 | NA | -0.0556135 | 0.0490921 | -0.2757215 | 1.0000000 | 0.0018833 | 0.2924349 | 0.4234423 | -0.1945230 | 0.0835863 | -0.1238858 |
| 0.1502153 | 0.3859266 | 0.8344035 | 0.3728913 | 0.4644850 | 0.0191203 | 0.1821875 | 0.2421988 | -0.1156707 | 0.1282056 | 0.0668798 | 0.2193127 | -0.3497214 | -0.1757411 | -0.0239250 | 0.2877064 | 0.2582761 | -0.0651121 | 0.0753131 | -0.0851461 | 0.2993491 | -0.2463999 | -0.1457239 | 0.1078506 | 0.3503402 | 0.4334738 | 0.1500682 | 0.1218255 | -0.4950337 | 0.1752678 | 0.3382779 | 0.1618602 | -0.2169466 | 0.1824478 | -0.0126270 | 0.1280101 | 0.3887465 | -0.2539913 | 0.3871837 | 0.1381970 | 0.3638499 | 0.3487999 | -0.1136109 | 0.1769045 | -0.0318145 | 0.2123422 | 0.3014163 | -0.1011977 | 0.2626842 | 0.1783958 | 0.0525889 | -0.1533037 | 0.3589719 | 0.4692248 | 0.3973442 | -0.0322612 | -0.0354767 | 0.3706249 | 0.4678883 | 0.1392599 | 0.0511238 | 0.3824376 | 0.4985053 | NA | 0.1226937 | -0.2178315 | -0.0220596 | 0.0018833 | 1.0000000 | -0.3357788 | -0.1081659 | 0.0820215 | -0.1440513 | 0.0910923 |
| -0.4558767 | -0.3815946 | -0.1270841 | -0.4562294 | -0.3828781 | 0.2898657 | -0.3724890 | -0.3735150 | 0.2798369 | 0.0533847 | -0.1186420 | -0.4070775 | 0.9577341 | 0.0597689 | 0.3390471 | -0.3276985 | -0.3000268 | -0.2444651 | -0.2477217 | 0.3327046 | -0.1687182 | 0.3353058 | 0.2726511 | -0.2697048 | -0.3494505 | -0.2658673 | -0.3083563 | -0.1423511 | 0.5237464 | -0.1463879 | -0.5786015 | 0.3553501 | 0.1552143 | -0.0037783 | 0.3050679 | 0.0309640 | 0.0709506 | -0.2359034 | -0.4701638 | -0.4111546 | -0.2596907 | -0.4340136 | -0.2099831 | -0.1739496 | 0.2527181 | 0.0912287 | -0.3683541 | 0.1883918 | -0.4802491 | -0.1263298 | 0.0157609 | 0.4026679 | -0.5431335 | -0.4938980 | -0.2872064 | -0.2957721 | -0.4071294 | -0.2472059 | -0.3901615 | -0.1087048 | -0.3052569 | -0.4667609 | -0.2269575 | NA | -0.2205765 | 0.1736432 | -0.3047779 | 0.2924349 | -0.3357788 | 1.0000000 | 0.3433165 | -0.3841887 | 0.2759276 | -0.1852764 |
| -0.0252301 | -0.3484926 | 0.0369669 | -0.2116838 | -0.1701500 | -0.2042048 | 0.1222387 | 0.2244327 | -0.3134659 | -0.0021327 | -0.0410406 | 0.0376302 | 0.3447483 | -0.2098273 | 0.1777997 | 0.3039393 | 0.0223691 | 0.0782634 | 0.2642131 | -0.0425600 | 0.4313171 | 0.1330878 | 0.4701264 | 0.0002488 | -0.3604930 | -0.1364015 | 0.1537550 | -0.0710535 | 0.6408035 | -0.0121996 | -0.0943950 | -0.2255494 | 0.2187536 | -0.0368573 | -0.1744413 | 0.1269461 | -0.0875100 | 0.0159297 | 0.0382020 | 0.0327847 | -0.1501779 | 0.0735449 | 0.1030904 | 0.0347554 | -0.2860123 | 0.1919677 | -0.1788735 | 0.1778815 | -0.0723979 | -0.3181458 | -0.3767530 | -0.2205197 | -0.0955137 | 0.1208411 | -0.1556881 | 0.1379181 | -0.0552895 | 0.0859054 | -0.1787915 | 0.4695141 | 0.0771761 | -0.2222804 | 0.0594833 | NA | -0.0201076 | -0.1038571 | 0.2162189 | 0.4234423 | -0.1081659 | 0.3433165 | 1.0000000 | -0.1595715 | 0.0364812 | -0.1577126 |
| 0.2778420 | -0.2000107 | -0.0177794 | 0.4211762 | -0.0383567 | -0.1799848 | 0.2678890 | 0.3937510 | -0.3529552 | -0.0115033 | 0.0692483 | 0.1114903 | -0.4910935 | -0.1787774 | -0.3356507 | 0.2730155 | -0.1671202 | 0.1800617 | 0.3301822 | -0.5278164 | -0.1515241 | -0.3967137 | -0.0302492 | 0.5269593 | -0.1984400 | -0.0042751 | 0.3473751 | -0.0621849 | -0.4046041 | 0.1481537 | 0.3289082 | -0.3745040 | 0.0331291 | -0.3100133 | -0.2058999 | 0.3765679 | 0.1691842 | -0.0083953 | 0.2274687 | 0.2706410 | 0.3942464 | 0.5052520 | 0.3492686 | 0.0886386 | -0.3421594 | -0.1466046 | 0.4698066 | 0.0394411 | 0.7274366 | 0.1327757 | 0.0211469 | -0.2018372 | 0.4251471 | 0.2983288 | 0.3595096 | 0.1002012 | 0.4829634 | 0.0696636 | 0.3425353 | 0.0463247 | 0.6369609 | 0.4265132 | -0.1339377 | NA | 0.2586064 | -0.3231911 | 0.2802598 | -0.1945230 | 0.0820215 | -0.3841887 | -0.1595715 | 1.0000000 | -0.4072655 | 0.2997837 |
| -0.4605798 | 0.1210353 | -0.0971718 | -0.3135288 | -0.2114159 | 0.2338191 | -0.4356152 | -0.4729837 | 0.3789646 | -0.0164765 | -0.1112860 | -0.2159744 | 0.2801776 | 0.2819261 | 0.3475057 | -0.2888526 | -0.0487550 | -0.3754741 | -0.3527568 | 0.3562557 | -0.2283080 | 0.2735754 | 0.0206588 | -0.2916853 | 0.1587826 | -0.3140646 | -0.3475484 | -0.1924271 | 0.1490965 | 0.0722144 | -0.5718451 | 0.2023409 | 0.3414679 | 0.4154974 | 0.2526991 | -0.2042609 | 0.1290065 | -0.1549324 | -0.3468863 | -0.2876959 | -0.2074087 | -0.3481542 | -0.3812995 | -0.4126114 | 0.3508309 | -0.0434705 | -0.2641353 | -0.0642934 | -0.4332333 | -0.4871924 | 0.2758113 | 0.3384805 | -0.4697215 | -0.4549956 | -0.1842491 | -0.4508286 | -0.3578762 | -0.0674496 | -0.1759720 | -0.2315824 | -0.4862412 | -0.3149523 | -0.1162564 | NA | -0.2757657 | 0.3221512 | -0.4440007 | 0.0835863 | -0.1440513 | 0.2759276 | 0.0364812 | -0.4072655 | 1.0000000 | -0.8235527 |
| 0.2257688 | -0.0063728 | 0.0839090 | 0.0668809 | 0.2136953 | -0.0538952 | 0.1743956 | 0.2599757 | -0.0207951 | 0.0621408 | -0.0511809 | -0.0521219 | -0.2112968 | -0.0843475 | -0.2880467 | 0.0361304 | -0.1005604 | 0.3111955 | 0.0512685 | -0.0530645 | 0.0760564 | -0.0063052 | -0.2138649 | 0.3591592 | -0.0172558 | 0.0757404 | 0.1513592 | 0.2045414 | -0.1504464 | -0.1430295 | 0.3083112 | 0.0456162 | -0.3144391 | -0.3732979 | -0.1236512 | -0.0903664 | -0.1108564 | 0.0461130 | 0.0429263 | -0.0031158 | 0.2240400 | 0.2728574 | 0.4316053 | 0.2920071 | -0.0390594 | 0.0459669 | 0.1421191 | -0.0784239 | 0.3522578 | 0.6249405 | -0.0449332 | -0.0728476 | 0.2683181 | 0.2193141 | 0.1828912 | 0.3021062 | 0.2339716 | -0.1363763 | 0.1449397 | -0.0212822 | 0.3757766 | 0.0714459 | 0.0349016 | NA | 0.3369285 | -0.1036240 | 0.2848627 | -0.1238858 | 0.0910923 | -0.1852764 | -0.1577126 | 0.2997837 | -0.8235527 | 1.0000000 |
Powyższa macierz pokazuje zależności pomiędzy konkretnymi kolumnami. Wygodniej jest jednak umieścić w dwóch kolumnach nazwy skorelowanych kolumn, a w trzeciej umieścić wartości współczynnika korelacji. Odfiltrowano również wartości mniejsze niż wskazaną wartość 0.8.
correl_table_filtered <- correl_table %>% mutate(from=names(correl_table)) %>%
pivot_longer(!from, names_to = "to", values_to = "count") %>%
filter(count >= corr_value, from != to) %>%
rename(corr=count)
kable(correl_table_filtered) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "500px")| from | to | corr |
|---|---|---|
| Hypersensitive.cardiac.troponinI | Direct.bilirubin | 0.8398009 |
| hemoglobin | hematocrit | 0.9127091 |
| Serum.chloride | serum.sodium | 0.8344035 |
| Prothrombin.time | International.standard.ratio | 0.9992779 |
| procalcitonin | basophil.count… | 0.8084308 |
| eosinophils… | Eosinophil.count | 0.8793301 |
| albumin | total.protein | 0.8015878 |
| albumin | calcium | 0.8633454 |
| Total.bilirubin | indirect.bilirubin | 0.8639484 |
| Total.bilirubin | Direct.bilirubin | 0.9373167 |
| Platelet.count | thrombocytocrit | 0.9577341 |
| Interleukin.8 | Tumor.necrosis.factor.U.03B1. | 0.8374025 |
| indirect.bilirubin | Total.bilirubin | 0.8639484 |
| Red.blood.cell.distribution.width | RBC.distribution.width.SD | 0.8246578 |
| total.protein | albumin | 0.8015878 |
| mean.corpuscular.volume | mean.corpuscular.hemoglobin | 0.8199013 |
| hematocrit | hemoglobin | 0.9127091 |
| Tumor.necrosis.factor.U.03B1. | Interleukin.8 | 0.8374025 |
| mean.corpuscular.hemoglobin.concentration | HCV.antibody.quantification | 0.8211721 |
| Urea | neutrophils.count | 0.8001254 |
| Urea | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | 0.8428192 |
| lymphocyte.count | X…lymphocyte | 0.8546539 |
| Eosinophil.count | eosinophils… | 0.8793301 |
| neutrophils.count | Urea | 0.8001254 |
| Direct.bilirubin | Hypersensitive.cardiac.troponinI | 0.8398009 |
| Direct.bilirubin | Total.bilirubin | 0.9373167 |
| Mean.platelet.volume | platelet.large.cell.ratio | 0.9873618 |
| Mean.platelet.volume | PLT.distribution.width | 0.8814381 |
| RBC.distribution.width.SD | Red.blood.cell.distribution.width | 0.8246578 |
| X…lymphocyte | lymphocyte.count | 0.8546539 |
| HCV.antibody.quantification | mean.corpuscular.hemoglobin.concentration | 0.8211721 |
| calcium | albumin | 0.8633454 |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Urea | 0.8428192 |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | Lactate.dehydrogenase | 0.8822205 |
| Lactate.dehydrogenase | Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | 0.8822205 |
| platelet.large.cell.ratio | Mean.platelet.volume | 0.9873618 |
| platelet.large.cell.ratio | PLT.distribution.width | 0.9212356 |
| Interleukin.6 | High.sensitivity.C.reactive.protein | 0.8657241 |
| PLT.distribution.width | Mean.platelet.volume | 0.8814381 |
| PLT.distribution.width | platelet.large.cell.ratio | 0.9212356 |
| International.standard.ratio | Prothrombin.time | 0.9992779 |
| basophil.count… | procalcitonin | 0.8084308 |
| mean.corpuscular.hemoglobin | mean.corpuscular.volume | 0.8199013 |
| High.sensitivity.C.reactive.protein | Interleukin.6 | 0.8657241 |
| serum.sodium | Serum.chloride | 0.8344035 |
| thrombocytocrit | Platelet.count | 0.9577341 |
Poniższa tabela wskaże, z iloma kolumnami dana kolumna jest skorelowana.
correl_table_n_corr <- correl_table_filtered %>%
group_by(from) %>%
summarise(n=n()) %>% filter(n >= 1) ## `summarise()` ungrouping output (override with `.groups` argument)kable(correl_table_n_corr) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "500px")| from | n |
|---|---|
| albumin | 2 |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | 2 |
| basophil.count… | 1 |
| calcium | 1 |
| Direct.bilirubin | 2 |
| Eosinophil.count | 1 |
| eosinophils… | 1 |
| HCV.antibody.quantification | 1 |
| hematocrit | 1 |
| hemoglobin | 1 |
| High.sensitivity.C.reactive.protein | 1 |
| Hypersensitive.cardiac.troponinI | 1 |
| indirect.bilirubin | 1 |
| Interleukin.6 | 1 |
| Interleukin.8 | 1 |
| International.standard.ratio | 1 |
| Lactate.dehydrogenase | 1 |
| lymphocyte.count | 1 |
| mean.corpuscular.hemoglobin | 1 |
| mean.corpuscular.hemoglobin.concentration | 1 |
| mean.corpuscular.volume | 1 |
| Mean.platelet.volume | 2 |
| neutrophils.count | 1 |
| Platelet.count | 1 |
| platelet.large.cell.ratio | 2 |
| PLT.distribution.width | 2 |
| procalcitonin | 1 |
| Prothrombin.time | 1 |
| RBC.distribution.width.SD | 1 |
| Red.blood.cell.distribution.width | 1 |
| Serum.chloride | 1 |
| serum.sodium | 1 |
| thrombocytocrit | 1 |
| Total.bilirubin | 2 |
| total.protein | 1 |
| Tumor.necrosis.factor.U.03B1. | 1 |
| Urea | 2 |
| X…lymphocyte | 1 |
% % % Cols to remove % Na podstawie ostatnich tabel, wybrano te kolumny które powinny zostać usunięte z tabeli, z powodu wysokiej korelacji.
correl_table_group_filter <- correl_table_filtered %>%
group_by(from) %>%
filter(from %in% correl_table_n_corr$from) %>%
ungroup() %>%
arrange(corr) %>%
distinct(corr, .keep_all = T)
columns_to_remove <- unique(list(correl_table_group_filter$to)[[1]])
kable(tibble(col_to_remove=columns_to_remove)) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "500px")| col_to_remove |
|---|
| neutrophils.count |
| total.protein |
| basophil.count… |
| mean.corpuscular.hemoglobin |
| HCV.antibody.quantification |
| RBC.distribution.width.SD |
| serum.sodium |
| Tumor.necrosis.factor.U.03B1. |
| Direct.bilirubin |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. |
| X…lymphocyte |
| calcium |
| indirect.bilirubin |
| High.sensitivity.C.reactive.protein |
| Eosinophil.count |
| PLT.distribution.width |
| Lactate.dehydrogenase |
| hematocrit |
| thrombocytocrit |
| platelet.large.cell.ratio |
| International.standard.ratio |
Poniższy wykres interaktywny umożliwi pokazanie zależności pomiędzy skorelowanymi kolumnami.
rows <- nrow(correl_table_group_filter)
cor_buttons <- list()
for (row in 1:rows){
xcol <- correl_table_filtered[row, ]$from
ycol <- correl_table_filtered[row, ]$to
ly <- list(method = "update",
label = paste(row),
args = list(list(
x=list(data_fill_summarise[[xcol]]),
y=list(data_fill_summarise[[ycol]])
),
list(yaxis = list(title = ycol), xaxis = list(title = xcol,
domain = c(0.1, 1)))
)
)
cor_buttons <- c(cor_buttons, list(ly))
}xcol1 <- correl_table_filtered[1, ]$from
ycol1 <- correl_table_filtered[1, ]$to
fig_cor <- plot_ly(data_fill_summarise,
x = ~data_fill_summarise[[xcol1]],
y = ~data_fill_summarise[[ycol1]], alpha = 0.3)
fig_cor <- fig_cor %>% add_markers(marker = list(line = list(color = "black", width = 0.5)))
fig_cor <- fig_cor %>% layout(
title = "Correlation plot",
xaxis = list(domain = c(0.1, 1), title = xcol1),
yaxis = list(title = ycol1),
updatemenus = list(
list(y = 0.8,
buttons = cor_buttons
)
)
)
fig_corKlasyfikator
Czyszczenie danych
Do analizy zostaną włączone identyfikator pacjenta (tylko do określenia, który wiersz należy do jakiego pacjenta), wiek, płeć, stan pacjenta (outcome, jako klasa dla klasyfikatora). Dodatkowo z danych krwii, zostaną wszystkie oprócz uzyskane z analizy korelacji a także, tych których wariancja jest za duża.
Dodanie kolumn i usunięcie kolumn o dużej korelacji.
join_data <- new_data_df[, c(1, 3, 4, 7)] %>% # select id, age, gender, outcome
group_by(PATIENT_ID) %>%
unique() %>%
ungroup()
data_join_filled <- data_fill_summarise %>%
select(-all_of(columns_to_remove)) %>%
left_join(y=join_data, by='PATIENT_ID')Usunięcie kolumn o dużej wariancji:
var_per_median <- 100
var_too_big <- data_processed_var_median %>% filter(abs(var) >= abs(var_per_median*median))
kable(var_too_big) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "50%")| col | var | median |
|---|---|---|
| Hypersensitive.cardiac.troponinI | 1.964032e+07 | 11.000 |
| Interleukin.2.receptor | 6.204319e+05 | 693.500 |
| Interleukin.10 | 4.643130e+03 | 6.000 |
| Total.bilirubin | 1.022797e+03 | 9.900 |
| Interleukin.8 | 4.337771e+03 | 16.450 |
| HBsAg | 1.868855e+03 | 0.010 |
| Direct.bilirubin | 5.660994e+02 | 4.600 |
| ferritin | 1.463976e+07 | 711.600 |
| aspartate.aminotransferase | 3.506764e+03 | 29.000 |
| Amino.terminal.brain.natriuretic.peptide.precursor.NT.proBNP. | 3.995339e+07 | 296.000 |
| Lactate.dehydrogenase | 1.010520e+05 | 325.500 |
| Interleukin.6 | 1.373100e+05 | 22.125 |
| Fibrin.degradation.products | 3.813263e+03 | 5.100 |
| X.U.03B3..glutamyl.transpeptidase | 5.301597e+03 | 31.000 |
| High.sensitivity.C.reactive.protein | 5.623949e+03 | 52.000 |
| glutamic.pyruvic.transaminase | 2.381624e+03 | 22.000 |
| creatinine | 1.654261e+04 | 74.500 |
data_unselected_high_var <- data_join_filled %>% select(-any_of(var_too_big$col))Pozostałe wartości puste zostaną wypełnione wartością ‘-1’.
data_replace_na <-data_unselected_high_var
data_replace_na[is.na(data_replace_na)] <- -1Ustawienie kolumn ‘outcome’ oraz ‘gender’ jako typ kategoryczny
data_clear <- ungroup(data_replace_na)
data_clear$outcome = as.factor(data_clear$outcome)
data_clear$gender = as.factor(data_clear$gender)
kable(head(data_clear)) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "100%")| PATIENT_ID | hemoglobin | Serum.chloride | Prothrombin.time | procalcitonin | eosinophils… | Alkaline.phosphatase | albumin | basophil… | Platelet.count | monocytes… | antithrombin | Red.blood.cell.distribution.width | neutrophils… | Quantification.of.Treponema.pallidum.antibodies | Prothrombin.activity | mean.corpuscular.volume | White.blood.cell.count | mean.corpuscular.hemoglobin.concentration | fibrinogen | Interleukin.1ß | Urea | lymphocyte.count | PH.value | Red.blood.cell.count | Corrected.calcium | Serum.potassium | glucose | Mean.platelet.volume | Thrombin.time | D.D.dimer | Total.cholesterol | Uric.acid | HCO3. | monocytes.count | globulin | X2019.nCoV.nucleic.acid.detection | Activation.of.partial.thromboplastin.time | HIV.antibody.quantification | ESR | eGFR | age | gender | outcome |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 131.0 | 101.4 | 13.9 | 0.09 | 0.60 | 54 | 33.3 | 0.30 | 141.0 | 9.0 | -1 | 11.9 | 65.8 | 0.05 | 91 | 92.7 | 9.050 | 347 | 3.28 | -1.0 | 7.25 | 1.555 | 6.000 | 4.075 | 2.385 | 4.47 | 7.35 | 11.50 | 19.2 | 1.56 | 3.90 | 388.0 | 26.15 | 0.62 | 34.7 | -1 | 37.9 | 0.09 | 41 | 68.75 | 73 | 1 | 0 |
| 2 | 148.0 | 99.9 | 14.3 | 0.09 | 0.00 | 43 | 35.3 | 0.10 | 301.0 | 4.8 | -1 | 12.2 | 81.5 | 0.06 | 87 | 91.3 | 8.690 | 348 | 5.58 | 5.0 | 5.50 | 1.220 | 6.000 | 4.630 | 2.320 | 4.78 | 5.91 | 10.10 | 17.0 | 0.96 | 4.93 | 178.0 | 24.50 | 0.43 | 35.2 | -1 | 42.3 | -1.00 | 40 | 92.60 | 61 | 1 | 0 |
| 3 | 110.5 | 99.1 | 13.6 | 0.06 | 0.05 | 47 | 34.9 | 0.05 | 203.5 | 6.0 | 84 | 12.7 | 70.7 | 0.07 | 94 | 94.5 | 4.515 | 330 | 5.33 | 5.0 | 2.90 | 1.235 | -1.000 | 3.555 | 2.170 | 3.34 | 9.02 | 11.15 | 16.7 | 0.98 | 3.28 | 151.0 | 22.30 | 0.28 | 33.4 | -1 | 34.8 | 0.10 | 66 | 77.20 | 70 | 2 | 0 |
| 4 | 84.0 | 100.8 | 16.3 | 0.38 | 1.50 | 66 | 33.9 | 0.20 | 94.0 | 3.8 | -1 | 11.3 | 82.0 | 0.04 | 68 | 118.9 | 5.990 | 372 | -1.00 | -1.0 | 7.50 | 0.750 | 6.000 | 1.900 | 2.340 | 3.84 | 5.77 | 10.50 | -1.0 | 1.26 | 2.56 | 250.0 | 22.80 | 0.23 | 31.4 | -1 | -1.0 | 0.11 | 72 | 82.00 | 74 | 1 | 0 |
| 5 | 134.0 | 99.7 | 14.6 | 0.02 | 3.30 | 78 | 40.2 | 0.30 | 380.0 | 7.0 | -1 | 12.0 | 58.9 | 0.05 | 83 | 87.8 | 6.140 | 338 | -1.00 | 16.4 | 2.30 | 1.870 | 6.000 | 4.510 | 2.260 | 3.26 | 4.84 | 9.80 | -1.0 | 0.42 | 2.49 | 302.0 | 25.60 | 0.43 | 30.1 | -1 | -1.0 | 0.08 | 15 | 120.00 | 29 | 2 | 0 |
| 6 | 108.0 | 95.6 | 12.4 | 0.10 | 0.70 | 80 | 39.4 | 0.70 | 160.0 | 13.2 | 105 | 12.0 | 61.9 | 0.04 | 117 | 87.3 | 4.380 | 341 | 4.93 | -1.0 | 9.97 | 1.030 | 7.438 | 3.630 | 2.210 | 3.39 | 5.63 | 12.40 | 16.0 | 0.60 | 4.05 | 237.6 | 23.20 | 0.58 | 27.7 | -1 | 45.0 | 0.08 | 27 | 47.30 | 81 | 2 | 0 |
Utworzenie klasyfikatora
Za pomocą ‘rfe’ można sprawdzić, które atrybuty mają największe znaczenie.
control <- rfeControl(functions=rfFuncs, method="repeatedcv", number=5, repeats=3)
results <- rfe(data_clear[, 2:42],
data_clear$outcome,
sizes=c(1:40),
rfeControl=control)print(results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (5 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 1 0.7922 0.5788 0.05059 0.10365
## 2 0.8505 0.6981 0.03581 0.07266
## 3 0.8735 0.7455 0.03475 0.07010
## 4 0.8883 0.7753 0.02543 0.05090
## 5 0.8957 0.7902 0.03125 0.06281
## 6 0.9031 0.8050 0.03210 0.06476
## 7 0.9096 0.8179 0.03979 0.08045
## 8 0.9114 0.8216 0.03612 0.07286
## 9 0.9160 0.8309 0.03816 0.07704
## 10 0.9114 0.8215 0.03870 0.07820
## 11 0.9133 0.8253 0.03928 0.07929
## 12 0.9197 0.8382 0.04021 0.08123
## 13 0.9216 0.8421 0.03933 0.07926
## 14 0.9197 0.8383 0.03578 0.07224
## 15 0.9197 0.8383 0.03868 0.07826
## 16 0.9124 0.8235 0.04091 0.08260
## 17 0.9151 0.8290 0.03929 0.07935
## 18 0.9188 0.8366 0.03800 0.07672
## 19 0.9161 0.8309 0.03881 0.07844
## 20 0.9188 0.8365 0.03611 0.07281
## 21 0.9188 0.8366 0.03718 0.07488
## 22 0.9142 0.8271 0.04011 0.08106
## 23 0.9170 0.8328 0.03797 0.07666
## 24 0.9188 0.8365 0.03687 0.07428
## 25 0.9161 0.8309 0.03772 0.07613
## 26 0.9188 0.8366 0.03789 0.07634
## 27 0.9197 0.8384 0.03835 0.07731
## 28 0.9206 0.8402 0.03929 0.07940
## 29 0.9197 0.8384 0.03654 0.07378
## 30 0.9216 0.8422 0.03890 0.07840
## 31 0.9216 0.8422 0.03599 0.07251
## 32 0.9216 0.8421 0.03331 0.06715
## 33 0.9206 0.8402 0.03678 0.07431
## 34 0.9206 0.8402 0.03889 0.07850
## 35 0.9207 0.8403 0.03312 0.06677
## 36 0.9188 0.8365 0.03677 0.07413
## 37 0.9225 0.8441 0.03526 0.07105 *
## 38 0.9215 0.8419 0.03685 0.07454
## 39 0.9188 0.8365 0.03457 0.06970
## 40 0.9206 0.8403 0.03399 0.06856
## 41 0.9198 0.8386 0.03796 0.07642
##
## The top 5 variables (out of 37):
## neutrophils..., procalcitonin, lymphocyte.count, monocytes..., D.D.dimerpredictors(results)## [1] "neutrophils..."
## [2] "procalcitonin"
## [3] "lymphocyte.count"
## [4] "monocytes..."
## [5] "D.D.dimer"
## [6] "albumin"
## [7] "White.blood.cell.count"
## [8] "age"
## [9] "eosinophils..."
## [10] "Prothrombin.time"
## [11] "Urea"
## [12] "Prothrombin.activity"
## [13] "Platelet.count"
## [14] "glucose"
## [15] "eGFR"
## [16] "Total.cholesterol"
## [17] "Serum.chloride"
## [18] "globulin"
## [19] "monocytes.count"
## [20] "basophil..."
## [21] "Red.blood.cell.distribution.width"
## [22] "Mean.platelet.volume"
## [23] "HCO3."
## [24] "Thrombin.time"
## [25] "mean.corpuscular.volume"
## [26] "Alkaline.phosphatase"
## [27] "fibrinogen"
## [28] "Activation.of.partial.thromboplastin.time"
## [29] "PH.value"
## [30] "Uric.acid"
## [31] "Red.blood.cell.count"
## [32] "ESR"
## [33] "antithrombin"
## [34] "Serum.potassium"
## [35] "hemoglobin"
## [36] "Corrected.calcium"
## [37] "Interleukin.1ß"plot(results, type=c("g", "o"))
Następnie można wybrać, na przykład 10 najlepszych atrybutów.
data_clear_selected_features <- data_clear %>%
select(outcome, predictors(results)[1:10])W kolejnym etapie można podzielić dane na treningowe oraz testowe.
data_to_fit <- data_clear_selected_features
set.seed(5555)
partition_set <- caret::createDataPartition(data_to_fit$outcome, p = .7, list = FALSE)
training <- data_to_fit[ partition_set,]## Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
## Convert to a vector.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.testing <- data_to_fit[-partition_set,]rfGrid <- expand.grid(mtry = 2:10)
gridCtrl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5)Do klasyfikacji, wykorzystano model ‘random forest’ z biblioteki caret:
set.seed(232323)
random_forest <- train(outcome ~ .,
data = training,
method = "rf",
trControl = gridCtrl,
tuneGrid = rfGrid,)
random_forest## Random Forest
##
## 254 samples
## 10 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 5 times)
## Summary of sample sizes: 229, 228, 228, 228, 230, 229, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9204641 0.8398740
## 3 0.9220974 0.8433467
## 4 0.9219359 0.8432428
## 5 0.9165205 0.8323898
## 6 0.9157538 0.8308132
## 7 0.9164923 0.8323552
## 8 0.9172615 0.8337711
## 9 0.9172615 0.8337881
## 10 0.9149231 0.8289229
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 3.Za pomoc ‘gridControl’ dobrano optymalny parametr ‘mtry’.
ggplotly(ggplot(random_forest) + theme_bw())Następnie, można przetestować działanie tego modelu na danych testowych:
rfClasses <- predict(random_forest, newdata = testing)
cmatrix <- confusionMatrix(data = rfClasses, testing$outcome)
cmatrix## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 53 5
## 1 5 44
##
## Accuracy : 0.9065
## 95% CI : (0.8348, 0.9543)
## No Information Rate : 0.5421
## P-Value [Acc > NIR] : 2.578e-16
##
## Kappa : 0.8118
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9138
## Specificity : 0.8980
## Pos Pred Value : 0.9138
## Neg Pred Value : 0.8980
## Prevalence : 0.5421
## Detection Rate : 0.4953
## Detection Prevalence : 0.5421
## Balanced Accuracy : 0.9059
##
## 'Positive' Class : 0
## prob <- as_tibble(predict(random_forest, newdata = testing, type = "prob")) %>% mutate(outcome=testing$outcome) %>% rename(Survived_probability="0", Died_probability="1")
kable(prob %>% arrange(Survived_probability)) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "400px")| Survived_probability | Died_probability | outcome |
|---|---|---|
| 0.000 | 1.000 | 1 |
| 0.000 | 1.000 | 1 |
| 0.000 | 1.000 | 1 |
| 0.000 | 1.000 | 1 |
| 0.000 | 1.000 | 1 |
| 0.002 | 0.998 | 1 |
| 0.002 | 0.998 | 1 |
| 0.002 | 0.998 | 1 |
| 0.006 | 0.994 | 1 |
| 0.006 | 0.994 | 1 |
| 0.008 | 0.992 | 1 |
| 0.016 | 0.984 | 1 |
| 0.018 | 0.982 | 1 |
| 0.024 | 0.976 | 1 |
| 0.026 | 0.974 | 1 |
| 0.028 | 0.972 | 1 |
| 0.034 | 0.966 | 0 |
| 0.038 | 0.962 | 1 |
| 0.044 | 0.956 | 1 |
| 0.046 | 0.954 | 1 |
| 0.050 | 0.950 | 1 |
| 0.050 | 0.950 | 1 |
| 0.052 | 0.948 | 1 |
| 0.054 | 0.946 | 1 |
| 0.062 | 0.938 | 1 |
| 0.064 | 0.936 | 1 |
| 0.068 | 0.932 | 1 |
| 0.076 | 0.924 | 1 |
| 0.082 | 0.918 | 1 |
| 0.086 | 0.914 | 1 |
| 0.094 | 0.906 | 1 |
| 0.124 | 0.876 | 1 |
| 0.140 | 0.860 | 1 |
| 0.148 | 0.852 | 1 |
| 0.158 | 0.842 | 1 |
| 0.160 | 0.840 | 1 |
| 0.174 | 0.826 | 1 |
| 0.252 | 0.748 | 1 |
| 0.280 | 0.720 | 1 |
| 0.282 | 0.718 | 1 |
| 0.310 | 0.690 | 0 |
| 0.314 | 0.686 | 1 |
| 0.324 | 0.676 | 0 |
| 0.372 | 0.628 | 1 |
| 0.392 | 0.608 | 0 |
| 0.402 | 0.598 | 1 |
| 0.420 | 0.580 | 0 |
| 0.462 | 0.538 | 1 |
| 0.464 | 0.536 | 1 |
| 0.510 | 0.490 | 1 |
| 0.548 | 0.452 | 1 |
| 0.548 | 0.452 | 1 |
| 0.606 | 0.394 | 0 |
| 0.622 | 0.378 | 1 |
| 0.630 | 0.370 | 0 |
| 0.638 | 0.362 | 0 |
| 0.646 | 0.354 | 1 |
| 0.650 | 0.350 | 0 |
| 0.656 | 0.344 | 0 |
| 0.668 | 0.332 | 0 |
| 0.688 | 0.312 | 0 |
| 0.714 | 0.286 | 0 |
| 0.758 | 0.242 | 0 |
| 0.776 | 0.224 | 0 |
| 0.778 | 0.222 | 0 |
| 0.848 | 0.152 | 0 |
| 0.864 | 0.136 | 0 |
| 0.870 | 0.130 | 0 |
| 0.872 | 0.128 | 0 |
| 0.876 | 0.124 | 0 |
| 0.876 | 0.124 | 0 |
| 0.890 | 0.110 | 0 |
| 0.894 | 0.106 | 0 |
| 0.900 | 0.100 | 0 |
| 0.900 | 0.100 | 0 |
| 0.912 | 0.088 | 0 |
| 0.922 | 0.078 | 0 |
| 0.926 | 0.074 | 0 |
| 0.948 | 0.052 | 0 |
| 0.954 | 0.046 | 0 |
| 0.960 | 0.040 | 0 |
| 0.968 | 0.032 | 0 |
| 0.970 | 0.030 | 0 |
| 0.972 | 0.028 | 0 |
| 0.980 | 0.020 | 0 |
| 0.980 | 0.020 | 0 |
| 0.984 | 0.016 | 0 |
| 0.988 | 0.012 | 0 |
| 0.990 | 0.010 | 0 |
| 0.990 | 0.010 | 0 |
| 0.990 | 0.010 | 0 |
| 0.996 | 0.004 | 0 |
| 0.996 | 0.004 | 0 |
| 0.996 | 0.004 | 0 |
| 0.996 | 0.004 | 0 |
| 0.998 | 0.002 | 0 |
| 0.998 | 0.002 | 0 |
| 0.998 | 0.002 | 0 |
| 0.998 | 0.002 | 0 |
| 0.998 | 0.002 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
| 1.000 | 0.000 | 0 |
Uzyskano trafność około 90%. Natomiast ważniejsze jest trafność czy pacjent przeżyje. Większe skutki niesie ze soba model, który zakwalifikowałby pacjenta, który by umarł (do klasy 1), jako zdolnego do przeżycia (do klasy 0) niż model, który stwierdzi, że pacjent umrze, choć ma duże szanse na przeżycie. W pierwszym przypadku może spowodować, że lekarze nie dbaliby o takiego pacjenta, myśląc, że przeżyje. W tym przypadku miara ‘False positive rate’ wynosi 0.102, a ‘Positive predictive value’ wynosi 91.4%
Ostatecznie, finalny model posiada 500 drzew, a parametr mtry=3:
random_forest$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 8.27%
## Confusion matrix:
## 0 1 class.error
## 0 125 12 0.08759124
## 1 9 108 0.07692308Najważniejsze atrybuty:
row_matrix_names <- rownames(random_forest$finalModel$importance)
values <- random_forest$finalModel$importance
kable(tibble(col=row_matrix_names, MeanDecreaseGini=values) %>%
arrange(desc(MeanDecreaseGini))) %>%
kable_styling(bootstrap_options = "basic", full_width = F) %>%
scroll_box(width = "100%", height = "400px")| col | MeanDecreaseGini |
|---|---|
| neutrophils… | 26.488327 |
| monocytes… | 19.261805 |
| lymphocyte.count | 15.843889 |
| White.blood.cell.count | 14.815900 |
| procalcitonin | 14.114685 |
| D.D.dimer | 13.211513 |
| albumin | 7.540005 |
| age | 6.234046 |
| Prothrombin.time | 4.507650 |
| eosinophils… | 3.513976 |